{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9b79bc6d",
   "metadata": {},
   "source": [
    "# Optional Lab - Regularized Cost and Gradient"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8c37f5e",
   "metadata": {},
   "source": [
    "## Goals\n",
    "In this lab, you will:\n",
    "- extend the previous linear and logistic cost functions with a regularization term.\n",
    "- rerun the previous example of over-fitting with a regularization term added.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8b2f78b9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-17T12:21:06.488702Z",
     "start_time": "2022-06-17T12:21:06.478699Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from plt_overfit import overfit_example, output\n",
    "from lab_utils_common import sigmoid\n",
    "np.set_printoptions(precision=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f99cf22",
   "metadata": {},
   "source": [
    "# Adding regularization\n",
    "<img align=\"Left\" src=\"../work/images/C1_W3_LinearGradientRegularized.png\"  style=\" width:400px; padding: 10px; \" >\n",
    "<img align=\"Center\" src=\"../work/images/C1_W3_LogisticGradientRegularized.png\"  style=\" width:400px; padding: 10px; \" >\n",
    "\n",
    "The slides above show the cost and gradient functions for both linear and logistic regression. Note:\n",
    "- Cost\n",
    "    - The cost functions differ significantly between linear and logistic regression, but adding regularization to the equations is the same.\n",
    "- Gradient\n",
    "    - The gradient functions for linear and logistic regression are very similar. They differ only in the implementation of $f_{wb}$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee604fd4",
   "metadata": {},
   "source": [
    "## Cost functions with regularization\n",
    "### Cost function for regularized linear regression\n",
    "\n",
    "The equation for the cost function regularized linear regression is:\n",
    "$$J(\\mathbf{w},b) = \\frac{1}{2m} \\sum\\limits_{i = 0}^{m-1} (f_{\\mathbf{w},b}(\\mathbf{x}^{(i)}) - y^{(i)})^2  + \\frac{\\lambda}{2m}  \\sum_{j=0}^{n-1} w_j^2 \\tag{1}$$ \n",
    "where:\n",
    "$$ f_{\\mathbf{w},b}(\\mathbf{x}^{(i)}) = \\mathbf{w} \\cdot \\mathbf{x}^{(i)} + b  \\tag{2} $$ \n",
    "\n",
    "\n",
    "Compare this to the cost function without regularization (which you implemented in  a previous lab), which is of the form:\n",
    "\n",
    "$$J(\\mathbf{w},b) = \\frac{1}{2m} \\sum\\limits_{i = 0}^{m-1} (f_{\\mathbf{w},b}(\\mathbf{x}^{(i)}) - y^{(i)})^2 $$ \n",
    "\n",
    "The difference is the regularization term,  <span style=\"color:blue\">\n",
    "    $\\frac{\\lambda}{2m}  \\sum_{j=0}^{n-1} w_j^2$ </span> \n",
    "    \n",
    "Including this term incentives gradient descent to minimize the size of the parameters. Note, in this example, the parameter $b$ is not regularized. This is standard practice.\n",
    "\n",
    "Below is an implementation of equations (1) and (2). Note that this uses a *standard pattern for this course*,   a `for loop` over all `m` examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f4bf799b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-17T12:21:06.520705Z",
     "start_time": "2022-06-17T12:21:06.494773Z"
    }
   },
   "outputs": [],
   "source": [
    "def compute_cost_linear_reg(X, y, w, b, lambda_ = 1):\n",
    "    \"\"\"\n",
    "    Computes the cost over all examples\n",
    "    Args:\n",
    "      X (ndarray (m,n): Data, m examples with n features\n",
    "      y (ndarray (m,)): target values\n",
    "      w (ndarray (n,)): model parameters  \n",
    "      b (scalar)      : model parameter\n",
    "      lambda_ (scalar): Controls amount of regularization\n",
    "    Returns:\n",
    "      total_cost (scalar):  cost \n",
    "    \"\"\"\n",
    "\n",
    "    m  = X.shape[0]\n",
    "    n  = len(w)\n",
    "    cost = 0.\n",
    "    for i in range(m):\n",
    "        f_wb_i = np.dot(X[i], w) + b                                   #(n,)(n,)=scalar, see np.dot\n",
    "        cost = cost + (f_wb_i - y[i])**2                               #scalar             \n",
    "    cost = cost / (2 * m)                                              #scalar  \n",
    " \n",
    "    reg_cost = 0\n",
    "    for j in range(n):\n",
    "        reg_cost += (w[j]**2)                                          #scalar\n",
    "    reg_cost = (lambda_/(2*m)) * reg_cost                              #scalar\n",
    "    \n",
    "    total_cost = cost + reg_cost                                       #scalar\n",
    "    return total_cost                                                  #scalar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9eef26c",
   "metadata": {},
   "source": [
    "Run the cell below to see it in action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e3e45983",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-17T12:21:06.536712Z",
     "start_time": "2022-06-17T12:21:06.524708Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regularized cost: 0.07917239320214275\n",
      "Regularized cost: 0.07917239320214275\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "X_tmp = np.random.rand(5,6)\n",
    "y_tmp = np.array([0,1,0,1,0])\n",
    "w_tmp = np.random.rand(X_tmp.shape[1]).reshape(-1,)-0.5\n",
    "b_tmp = 0.5\n",
    "lambda_tmp = 0.7\n",
    "cost_tmp = compute_cost_linear_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\n",
    "\n",
    "print(\"Regularized cost:\", cost_tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "041749de",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table>\n",
    "  <tr>\n",
    "    <td> <b>Regularized cost: </b> 0.07917239320214275 </td>\n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "199c887c",
   "metadata": {},
   "source": [
    "### Cost function for regularized logistic regression\n",
    "For regularized **logistic** regression, the cost function is of the form\n",
    "$$J(\\mathbf{w},b) = \\frac{1}{m}  \\sum_{i=0}^{m-1} \\left[ -y^{(i)} \\log\\left(f_{\\mathbf{w},b}\\left( \\mathbf{x}^{(i)} \\right) \\right) - \\left( 1 - y^{(i)}\\right) \\log \\left( 1 - f_{\\mathbf{w},b}\\left( \\mathbf{x}^{(i)} \\right) \\right) \\right] + \\frac{\\lambda}{2m}  \\sum_{j=0}^{n-1} w_j^2 \\tag{3}$$\n",
    "where:\n",
    "$$ f_{\\mathbf{w},b}(\\mathbf{x}^{(i)}) = sigmoid(\\mathbf{w} \\cdot \\mathbf{x}^{(i)} + b)  \\tag{4} $$ \n",
    "\n",
    "Compare this to the cost function without regularization (which you implemented in  a previous lab):\n",
    "\n",
    "$$ J(\\mathbf{w},b) = \\frac{1}{m}\\sum_{i=0}^{m-1} \\left[ (-y^{(i)} \\log\\left(f_{\\mathbf{w},b}\\left( \\mathbf{x}^{(i)} \\right) \\right) - \\left( 1 - y^{(i)}\\right) \\log \\left( 1 - f_{\\mathbf{w},b}\\left( \\mathbf{x}^{(i)} \\right) \\right)\\right] $$\n",
    "\n",
    "As was the case in linear regression above, the difference is the regularization term, which is    <span style=\"color:blue\">\n",
    "    $\\frac{\\lambda}{2m}  \\sum_{j=0}^{n-1} w_j^2$ </span> \n",
    "\n",
    "Including this term incentives gradient descent to minimize the size of the parameters. Note, in this example, the parameter $b$ is not regularized. This is standard practice. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c42a4efe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-17T12:21:06.568717Z",
     "start_time": "2022-06-17T12:21:06.543716Z"
    }
   },
   "outputs": [],
   "source": [
    "def compute_cost_logistic_reg(X, y, w, b, lambda_ = 1):\n",
    "    \"\"\"\n",
    "    Computes the cost over all examples\n",
    "    Args:\n",
    "    Args:\n",
    "      X (ndarray (m,n): Data, m examples with n features\n",
    "      y (ndarray (m,)): target values\n",
    "      w (ndarray (n,)): model parameters  \n",
    "      b (scalar)      : model parameter\n",
    "      lambda_ (scalar): Controls amount of regularization\n",
    "    Returns:\n",
    "      total_cost (scalar):  cost \n",
    "    \"\"\"\n",
    "\n",
    "    m,n  = X.shape\n",
    "    cost = 0.\n",
    "    for i in range(m):\n",
    "        z_i = np.dot(X[i], w) + b                                      #(n,)(n,)=scalar, see np.dot\n",
    "        f_wb_i = sigmoid(z_i)                                          #scalar\n",
    "        cost +=  -y[i]*np.log(f_wb_i) - (1-y[i])*np.log(1-f_wb_i)      #scalar\n",
    "             \n",
    "    cost = cost/m                                                      #scalar\n",
    "\n",
    "    reg_cost = 0\n",
    "    for j in range(n):\n",
    "        reg_cost += (w[j]**2)                                          #scalar\n",
    "    reg_cost = (lambda_/(2*m)) * reg_cost                              #scalar\n",
    "    \n",
    "    total_cost = cost + reg_cost                                       #scalar\n",
    "    return total_cost                                                  #scalar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "983a06a9",
   "metadata": {},
   "source": [
    "Run the cell below to see it in action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1495f382",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-17T12:21:06.584724Z",
     "start_time": "2022-06-17T12:21:06.572719Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regularized cost: 0.6850849138741673\n",
      "Regularized cost: 0.6850849138741673\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "X_tmp = np.random.rand(5,6)\n",
    "y_tmp = np.array([0,1,0,1,0])\n",
    "w_tmp = np.random.rand(X_tmp.shape[1]).reshape(-1,)-0.5\n",
    "b_tmp = 0.5\n",
    "lambda_tmp = 0.7\n",
    "cost_tmp = compute_cost_logistic_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\n",
    "\n",
    "print(\"Regularized cost:\", cost_tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8c1dadd",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table>\n",
    "  <tr>\n",
    "    <td> <b>Regularized cost: </b> 0.6850849138741673 </td>\n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "021753e0",
   "metadata": {},
   "source": [
    "## Gradient descent with regularization\n",
    "The basic algorithm for running gradient descent does not change with regularization, it is:\n",
    "$$\\begin{align*}\n",
    "&\\text{repeat until convergence:} \\; \\lbrace \\\\\n",
    "&  \\; \\; \\;w_j = w_j -  \\alpha \\frac{\\partial J(\\mathbf{w},b)}{\\partial w_j} \\tag{1}  \\; & \\text{for j := 0..n-1} \\\\ \n",
    "&  \\; \\; \\;  \\; \\;b = b -  \\alpha \\frac{\\partial J(\\mathbf{w},b)}{\\partial b} \\\\\n",
    "&\\rbrace\n",
    "\\end{align*}$$\n",
    "Where each iteration performs simultaneous updates on $w_j$ for all $j$.\n",
    "\n",
    "What changes with regularization is computing the gradients."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c59281f3",
   "metadata": {},
   "source": [
    "### Computing the Gradient with regularization (both linear/logistic)\n",
    "The gradient calculation for both linear and logistic regression are nearly identical, differing only in computation of $f_{\\mathbf{w}b}$.\n",
    "$$\\begin{align*}\n",
    "\\frac{\\partial J(\\mathbf{w},b)}{\\partial w_j}  &= \\frac{1}{m} \\sum\\limits_{i = 0}^{m-1} (f_{\\mathbf{w},b}(\\mathbf{x}^{(i)}) - y^{(i)})x_{j}^{(i)}  +  \\frac{\\lambda}{m} w_j \\tag{2} \\\\\n",
    "\\frac{\\partial J(\\mathbf{w},b)}{\\partial b}  &= \\frac{1}{m} \\sum\\limits_{i = 0}^{m-1} (f_{\\mathbf{w},b}(\\mathbf{x}^{(i)}) - y^{(i)}) \\tag{3} \n",
    "\\end{align*}$$\n",
    "\n",
    "* m is the number of training examples in the data set      \n",
    "* $f_{\\mathbf{w},b}(x^{(i)})$ is the model's prediction, while $y^{(i)}$ is the target\n",
    "\n",
    "      \n",
    "* For a  <span style=\"color:blue\"> **linear** </span> regression model  \n",
    "    $f_{\\mathbf{w},b}(x) = \\mathbf{w} \\cdot \\mathbf{x} + b$  \n",
    "* For a <span style=\"color:blue\"> **logistic** </span> regression model  \n",
    "    $z = \\mathbf{w} \\cdot \\mathbf{x} + b$  \n",
    "    $f_{\\mathbf{w},b}(x) = g(z)$  \n",
    "    where $g(z)$ is the sigmoid function:  \n",
    "    $g(z) = \\frac{1}{1+e^{-z}}$   \n",
    "    \n",
    "The term which adds regularization is  the <span style=\"color:blue\">$\\frac{\\lambda}{m} w_j $</span>."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a581c48",
   "metadata": {},
   "source": [
    "### Gradient function for regularized linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2f22e837",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-17T12:21:06.615728Z",
     "start_time": "2022-06-17T12:21:06.589723Z"
    }
   },
   "outputs": [],
   "source": [
    "def compute_gradient_linear_reg(X, y, w, b, lambda_): \n",
    "    \"\"\"\n",
    "    Computes the gradient for linear regression \n",
    "    Args:\n",
    "      X (ndarray (m,n): Data, m examples with n features\n",
    "      y (ndarray (m,)): target values\n",
    "      w (ndarray (n,)): model parameters  \n",
    "      b (scalar)      : model parameter\n",
    "      lambda_ (scalar): Controls amount of regularization\n",
    "      \n",
    "    Returns:\n",
    "      dj_dw (ndarray (n,)): The gradient of the cost w.r.t. the parameters w. \n",
    "      dj_db (scalar):       The gradient of the cost w.r.t. the parameter b. \n",
    "    \"\"\"\n",
    "    m,n = X.shape           #(number of examples, number of features)\n",
    "    dj_dw = np.zeros((n,))\n",
    "    dj_db = 0.\n",
    "\n",
    "    for i in range(m):                             \n",
    "        err = (np.dot(X[i], w) + b) - y[i]                 \n",
    "        for j in range(n):                         \n",
    "            dj_dw[j] = dj_dw[j] + err * X[i, j]               \n",
    "        dj_db = dj_db + err                        \n",
    "    dj_dw = dj_dw / m                                \n",
    "    dj_db = dj_db / m   \n",
    "    \n",
    "    for j in range(n):\n",
    "        dj_dw[j] = dj_dw[j] + (lambda_/m) * w[j]\n",
    "\n",
    "    return dj_db, dj_dw"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7a7e9f3",
   "metadata": {},
   "source": [
    "Run the cell below to see it in action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0dbaf84c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-17T12:21:06.646736Z",
     "start_time": "2022-06-17T12:21:06.619730Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dj_db: 0.6648774569425727\n",
      "Regularized dj_dw:\n",
      " [0.29653214748822276, 0.4911679625918033, 0.21645877535865857]\n",
      "dj_db: 0.6648774569425727\n",
      "Regularized dj_dw:\n",
      " [0.29653214748822276, 0.4911679625918033, 0.21645877535865857]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "X_tmp = np.random.rand(5,3)\n",
    "y_tmp = np.array([0,1,0,1,0])\n",
    "w_tmp = np.random.rand(X_tmp.shape[1])\n",
    "b_tmp = 0.5\n",
    "lambda_tmp = 0.7\n",
    "dj_db_tmp, dj_dw_tmp =  compute_gradient_linear_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\n",
    "\n",
    "print(f\"dj_db: {dj_db_tmp}\", )\n",
    "print(f\"Regularized dj_dw:\\n {dj_dw_tmp.tolist()}\", )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7205162",
   "metadata": {},
   "source": [
    "**Expected Output**\n",
    "```\n",
    "dj_db: 0.6648774569425726\n",
    "Regularized dj_dw:\n",
    " [0.29653214748822276, 0.4911679625918033, 0.21645877535865857]\n",
    " ```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b277c6e",
   "metadata": {},
   "source": [
    "### Gradient function for regularized logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7b2ec8d0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-17T12:21:06.678743Z",
     "start_time": "2022-06-17T12:21:06.650736Z"
    }
   },
   "outputs": [],
   "source": [
    "def compute_gradient_logistic_reg(X, y, w, b, lambda_): \n",
    "    \"\"\"\n",
    "    Computes the gradient for linear regression \n",
    " \n",
    "    Args:\n",
    "      X (ndarray (m,n): Data, m examples with n features\n",
    "      y (ndarray (m,)): target values\n",
    "      w (ndarray (n,)): model parameters  \n",
    "      b (scalar)      : model parameter\n",
    "      lambda_ (scalar): Controls amount of regularization\n",
    "    Returns\n",
    "      dj_dw (ndarray Shape (n,)): The gradient of the cost w.r.t. the parameters w. \n",
    "      dj_db (scalar)            : The gradient of the cost w.r.t. the parameter b. \n",
    "    \"\"\"\n",
    "    m,n = X.shape\n",
    "    dj_dw = np.zeros((n,))                            #(n,)\n",
    "    dj_db = 0.0                                       #scalar\n",
    "\n",
    "    for i in range(m):\n",
    "        f_wb_i = sigmoid(np.dot(X[i],w) + b)          #(n,)(n,)=scalar\n",
    "        err_i  = f_wb_i  - y[i]                       #scalar\n",
    "        for j in range(n):\n",
    "            dj_dw[j] = dj_dw[j] + err_i * X[i,j]      #scalar\n",
    "        dj_db = dj_db + err_i\n",
    "    dj_dw = dj_dw/m                                   #(n,)\n",
    "    dj_db = dj_db/m                                   #scalar\n",
    "\n",
    "    for j in range(n):\n",
    "        dj_dw[j] = dj_dw[j] + (lambda_/m) * w[j]\n",
    "\n",
    "    return dj_db, dj_dw  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c574f24",
   "metadata": {},
   "source": [
    "Run the cell below to see it in action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "767c3f4c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-17T12:21:06.694935Z",
     "start_time": "2022-06-17T12:21:06.683743Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dj_db: 0.341798994972791\n",
      "Regularized dj_dw:\n",
      " [0.17380012933994293, 0.32007507881566943, 0.10776313396851499]\n",
      "dj_db: 0.341798994972791\n",
      "Regularized dj_dw:\n",
      " [0.17380012933994293, 0.32007507881566943, 0.10776313396851499]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "X_tmp = np.random.rand(5,3)\n",
    "y_tmp = np.array([0,1,0,1,0])\n",
    "w_tmp = np.random.rand(X_tmp.shape[1])\n",
    "b_tmp = 0.5\n",
    "lambda_tmp = 0.7\n",
    "dj_db_tmp, dj_dw_tmp =  compute_gradient_logistic_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\n",
    "\n",
    "print(f\"dj_db: {dj_db_tmp}\", )\n",
    "print(f\"Regularized dj_dw:\\n {dj_dw_tmp.tolist()}\", )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137d0f73",
   "metadata": {},
   "source": [
    "**Expected Output**\n",
    "```\n",
    "dj_db: 0.341798994972791\n",
    "Regularized dj_dw:\n",
    " [0.17380012933994293, 0.32007507881566943, 0.10776313396851499]\n",
    " ```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "533e58c7",
   "metadata": {},
   "source": [
    "## Rerun over-fitting example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c305a530",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-17T12:21:08.122892Z",
     "start_time": "2022-06-17T12:21:06.700454Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31c3cc347a5c4517a863301715a87e27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31c3cc347a5c4517a863301715a87e27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Gjh1L9erVKVy4MEOHDs3UOhKTicTfoz6nTp2ifv36NG/enP/973+4uLgQFhZG27ZtsyRGKysrDh48yPbt29mzZw9bt27ls88+Y9q0aYwdO5aEhASaN2/OzJkzU732RRrMtmjRgs2bN7Nr1y4qV65MyZIl6dGjB2vXriUiIoJ69eqRP3/+TK83pRf9/FO+Tt93KfE3mNY2vvvuO3r37s2WLVvw9/dnxowZtGzZko0bN5KQkECBAgX09uAqVKjQC8WcnSQByEZFixbllVdeYdasWYwaNSrV1faUKVNwdHRM1gXnzTff5IcffmD58uXs37+fuXPn6p6rUaMGixYtomDBgsmuZrNCvnz5AIiIiEi1POWyF1GuXDldcuTu7v7S60spLi6OXr160bx5czw8PHj99depXr065cuXB2DTpk0MHz6cqVOn6l6zceNGg8eR6ODBg1SsWJGiRYumeq5GjRoAXL9+XW+imFk1atTA09OTCRMm0K1bt1RJRFRUFE+fPuXAgQOEhYWxa9cuvW1UIO3vQY0aNTh79myaSVji+3z+/Hnd/qVUtWpVQkNDuXLliq5raVxcHP7+/rz++usZ3l+AatWqsXjxYkJDQ3VXgaAd7ENDQ/Hz82Pjxo107NhR91xmk6vEGM+ePZvmVfmOHTsoVKgQW7du1Z1gUl4Z6ntPQ0JCXijG2NhYrKys6NChAx06dABg+PDh/P7774wdO5YaNWqwZMkSXFxc0jwxZ+Y33aJFCyZPnsyyZct0vZd69OjB1KlTsbKySrfGKq3vUlZKbBuRVOLjypUrpyqvlCIhIYG6detSt25dAFauXEnv3r15/PgxNWrU4PHjx0RGRqaqscsNpBFgNps5cybx8fHUrVuXFStWcPbsWbZt20a7du3YuXMnv/zyS7IfeaVKlWjUqBFDhw6ladOmutoBgP79+1O6dGnatWvHhg0buHjxInv27GHEiBHPvXLNLCcnJ5ycnJgzZw6nT59m69atgPaDun37NvPnz+f06dMcO3bshdbv4OBA165d+eSTT9i3bx/Hjh3jk08+yXB/8/DwcIKDg1P9xcfHA/DRRx9x48YN5s6dy/jx46latSo9e/YkOjoaAC8vL3bt2sWRI0c4deoUM2bMYN68eS+0L/qcOnWKPXv2EBAQwJw5c5g5c6beWxag1QD16dOHoUOH8ssvv3Du3DmOHTvGl19+mayr47///oulpSWTJk1Kd9tmZmbMnz+fu3fvUqtWLX7//XfOnTvHyZMnmTVrFlWrVuXMmTNUqlQJMzMz5s2bR0BAANu2beO1115Ltq60vgcTJkxg06ZNvPXWWxw9epSAgACWLVtG586dAahZsya1a9dm9OjR+Pn5cejQIYYPH65rRAjQvHlzGjVqRJ8+fdi7dy8nTpzA19eXx48f8/bbb2fq/e7bt6/uO7Vv3z7Onz/PnDlzeOedd3B0dMTJyYkVK1Zw7tw5Dh06xIcffpjpcTiqVauGt7c3o0aN4siRIxw8eJAhQ4Zw9epVXRkvLy8ePHjAsmXLCAgI4M8//2TIkCHJ1uPm5oaNjQ0//PADZ86cYffu3S8c45YtW3jllVfYuHEjly9f5sCBAxw4cIB69eoBMHLkSGJiYujQoQM7d+7k4sWLbNu2jb59++pqIKtWrcrx48f5888/OXHiBGfPnk1ze/Xq1cPa2pqVK1fqbk1WqlQJV1dX1qxZk6q6/Xn7ndU++ugjjh49ygcffMDp06d1DQD79eunt/o/LCyMypUrs2DBAi5cuMCpU6dYu3YtHh4eFCxYkNatW9OoUSO6devG8uXLCQgI4PDhw3z88ccsXLgwy/fnpRmr8YEpu379uho6dKhycXFRVlZWqmjRoqpz587q6NGjessvWrRI1/UppTt37qghQ4YoZ2dnZWVlpcqUKaP69++vaySVViO9zHYDVEqprVu3Knd3d2Vtba3rsqOUUqNHj1YODg7Kzs5OjRs3TimlvxHg5MmTk20jZWOjO3fuqI4dO+q6S40cOVLVrVtXDRs2TG+MiXx8fPR2T0vcv7Vr1ypLS0td1yullPr3339VkSJF1FtvvaWUUurmzZuqXbt2ys7OTjk5OanXX39dvfHGG8neO32No1I2GFJKqWHDhqlGjRoli69y5cqqevXqytbWVpUpU0bXHS1pmZTdACdNmqTc3NyUlZWVcnJyUm3btlV79+7VlcloN8BEFy9eVL6+vrrvXcGCBVWTJk3UrFmzdN2g5s2bp1xcXJStra2qVauWWrp0qQLU0qVLdetJ63uwY8cO5ePjo/Lnz6/y58+vqlevrr788stk22/SpImysrJSxYsXV+PHj1cuLi5q6tSpujJ3795Vffv2VYULF1a2traqWbNm6tixY7rn0/re6vu+JnbfKlmypLK3t1fe3t5q9erVSimlDh48qGrUqKFsbGxU+fLl1cSJE1W9evWSfQYZ6QZ45coV5ePjo2xsbJS7u7uaOnWqKlOmTLLP5PPPP1dOTk7Kzs5ONW7cWM2ZMydVQ8rFixerMmXKKGtra9W0adMMx5hScHCw6t+/vypbtqyysbFRLi4u6p133knWze3y5cvqtddeU0WLFlU2NjaqQoUKatiwYbqGbJGRkapv376qYMGCqkCBAmr27Nnpvgft27dXtWrVSrbss88+UzY2NioyMlK3LGUjwLT2W9/vbOHChcrCwiLdOPQd6/R9X3bs2KHq1KmjrK2tVbFixdSIESNUeHi47vmkv+mYmBg1duxYValSJWVnZ6eKFSumunXrlqyh6uPHj9W7776rypQpo6ysrFTJkiVVt27d1OnTp9ONNycwU0rPTUghcghPT08GDx7Mhx9+aOxQXlizZs2oWLEiv/76q7FDyVFiY2NxcHBg/vz5qWoahBBZT9oAiBzj4MGDXL9+XVcVvXLlSq5fv07v3r2NHZowgM2bNxMdHY27uzvR0dHMnj0be3t72rdvb+zQhDBJkgCIHOPBgwd8+umn3Lp1C3t7e2rWrMmBAwdSdcUTudP169f58ssvuXv3LkWLFqV+/fp6ezoIIbKH3AIQQgghTJDJ1ABkdLYzIYQQIq/RNy6BdAMUQgghTJAkAEIIIYQJMplbAEnlhiEahRBCiJfxvFvfUgMghBBCmKAclQCcOHGCatWqpfn8li1bqFKlCh4eHkyZMkW3/P79+7Rt2xZ3d3fatm2baqIXIYQQQiSXYxKAsWPH0rp1a91MaCk9ffqU4cOHs2vXLs6dO8fWrVs5ceIEAB988AFdu3bl0qVLdO3alYkTJ2Zj5EIIIUTuk2MSgO+++47jx4+n+byfnx+1atXC2dkZS0tLevTowZYtWwDYvXs3vXr1AtBN25iXrbwC9daC3S9QdAG0+BOWX9aem+gP/Xdp/993Cyx/fv76fP+C/2VuHpRskZm4zObAlef09MxIGUNZFACN16f9vOtvsOtGxtYV/FiLPU5/bpxM0s/fUPTF+rz9S6rZn/Dref3P7b0JpZek/dpFAWA+R/se90uyX5v/Be+1YP8LOC7Ufg8z/nn2fHqf9aRj0HLDs8fTToLTQsg3D7ZfS72/O65r27f4WduXtDyKhrabIP8v2joM4XmfpyG/0/8+gWIL4a8Mfi8z6qez2mecbx68shFuhD977p2D0GVr5teZ0f3OzPfUFOWaRoC3bt3CyclJ97hYsWJcvqyd9e7fv0/hwoUBrYFfXr4FMP0fmHYK5jSFFqUgNkE7GM49D33ckpf1KQlxbxklTCEMZqAHLEoyqZx/CAzeA8tbQZMS8DQOdt+Azdcytr7xdZ79/1E0fHgE/nkNKjtAvJ4k6xUX7Xe09yZMTGeyy8UXITwW7r0O5i82fb1RDd8P71aDFqUNt84jd2C8P2xuDx6FYdwRGLIXtv83w/H3DaH2Gu2ipldFw21XZEyOqQHICHPz5OHGxMQA6ObZTrk8rwmLhk+PwtKW0MkV8ltBYRvo5w47OqYuv/dm8iuRa0+0bNthAZRaAiMPpH5NTLx2dfTm3rTjWBgAbsug8HxtfbefPnvObA587g9VVkDBX7WrF30HVXh2ZTvBDyr8rl19fO6f+e0mXs15Lteu1ObpudpMq0x6+5LS5/5QYjHYzoMyS+Hrk8+ei0uAj49oV5JOC2HmmeSvPREK9ddqV4d11sCDqLS3A/D7Jaj4OxT4FfqnmCU1rTj23dKubpdd1vYx8co6vbgN5ehd7Yq8wK/a1fjRu2mX/e4UuCzR3vOPj2R+W/tvQYPi2onKykL7DXSvAAtSTD2/8or+7+FEf612CbSTD0DN1dp789Z+7Uq4zSbtPfw0gzVQ+27Bu4fg8B3tPZj0X6LwvN/KF8eh2kqwmav9PvW59RR6btd+t1VWaAmQPilrWn49n7zGIuAhtN4IheaD+zJYE/jsuROhcOoevFddexwTr9UuHrqdPI588+BeZMbeE4DVV2GQB3gX1z6nSfW0ZO2hNgs3FubwVX34Mu3KXwB2XoeqK7Xfj88fyZ/79bz2fbKdB86L4L1DkKC0z3HIXjh0R/ssLX+G6Pi0y+cI1wKydXO5JgFwdnbm3r17usehoaE4OzsDUKRIEcLDtXqlsLAwHBwcjBJjVjtyF+ytoHmp1M9ZW6T/2vgE6LQVqjjAtQGw71WwS1H/k6C0k42jLcz10b+efbe0LH5Fa7gxEFwLQN8UVZTXwmFLB7jQG/bcgj+D048tnyX4dYe/OsPMs1rikpnt7tamnCegj3alNtQr9ev1lcnIviTVqyKc7gmRb8LBLjDrDPj9d6L74bR2YPPvob2/bZNMXxAeCx22wBuV4MFgWNkabNP5vPxD4J1D2lXvg9fhk1oZi8OnpHZ129dN28cbA58ftyE8jIb2m2FMNQjxhbHVtcf6kpzVgTD7LGztoJX19cz89uo6wfbr8NUJOB6qHdT1ufr4+d/D3Z20f6OGau/X/OZQtoB2hRr3FnzpnbGYUr73X3pn7Pt1+RFsag9P3oBS+fWvO+iJ9n29MUA7mfbdlfkT1tNY7eTfqwLcHQS/NNNqUYIfa8+vDtS+J7b/HROsLbQaxcUXn61jyUXoUBYc82mPnRdpJ1F9f4nJ1qVHUDbJVA/F8kEh6+TV96+4QGiUVlaf6+HQcwdM8YZHg2F2k+TPtygNR7pBxJtwppf2vq8J1D7H+c2gkbP2mcS9BTYWaZc3qqdhoBTcvwVTp0BcXLZsNkcnAGFhYVy7ptXreXt74+/vT0hICHFxcaxZs4aWLVsC0KJFC1auXAnAihUrdMvzmntRUKbAi73WLwTuRmgZuL0VVCwE3zRIXuadg9rJ6reWaVdhLr0Igz2hdjFtPVPraxn2v0muXsbV0uIsZa9dqQU8TD+292tAUVuoWhS6loNt119su5mV2XXGJcBHR8B9OVRbBTefwvn/9m3hRZhYVzvo2FqCW5KhJrb8C6Xzwxte2gGoQiGws0o7roUB8LoHNC6hXeF6Fcl4HJmNO6Pab05+gH9z37PnNv+rVe/2cdOSuV4VoVIR2Bisf9/G1oAqRbWTjGfhzMUB0LSklhxtCIaG68D+V+2qMGVSk9nvoaFl5Pv1WR0tRmsL7WpYn0bO0NpF+86MqabdQ7/8KHOxbPoXyhXUvoO2llrC0rjEs7YOR+5qtxST8vWAVYEQ9d+5aPF/+5Pojq+WOOn7S9yXiDjtO5FUfivtOJPI3AwaO2sx6LP8MrQurdV6Wllo352krMzhyxNQZSV4LIfT99P/fme2fJY7dwiGVoV/9kGtlvDJp2BllS1JQI5JAMaPH0/nzp0JDAykTp067Nu3j/Xr1zNwoHYZY29vz8yZM2nevDleXl60bt0aHx/tMnXatGmsXLkSd3d31q5dyzfffGPMXckyDjYQmonqt6Suh4OLfdon9sUXYdZZGOKp/cjScvMplE6S0eez1E7eN9OoOre1gJgMNF5L5Gir/8oxs9vNiMysMzwWmm3Qrl52dNSuXpuWfNYwL+gxlEsjOQt6oh18MyrocdrlnxfHy5ZPy5YOyQ/wvySpIUr5PoL2XdP3Pqb3PmVGr4pwuBuEvwF+3cCzCLTfolVd65PZ76EhZMV31soCitjA/ejMve7fJ1rykTSJ23UD7vx3PLkbCWVSfIZ1nLTP8Y8g7V7+41ho45K57dpZpn7fo+Mhf4qkwMVeu0DRJ73fg1Jaw8IH0bCuDdweBP3d0/5+Z7Z8lkpIgBVfwefdYMRMqJHiHlY2JAE5phHgpEmTmDRpUrJlPj4++Pr66h536tSJTp06pXptsWLF2LFjR1aHaHT1neFOhPbDbZWioU6CSr/hUWl7LQlIq1wVB5jVBAb9pV2h1nDUv55S+bX1JIqMg/tRaVdfZlbQE6heNPXy523X3Ez7cacnZZnM7Mu5B9p792Nj/et2tNWqMdN8LhOJm2O+tMs/Lw4LM1CZKP9C4uJg83Yo1x4wo1R+7SSR1PVwaKvnZJHZ90KfmPhnt7ysLKBmMfiuodauIyxGq2Z+GRYZ+C5lRFb8ViJite+ZviTK2hyi0ziRlcoPzUo+uxWmj4We48IgD+3ioGwBGOSevJbCcaHWiFKf6P9qAdwLw4UkV9ehkVpNZsUUg7Gmd0vDMV/yngNJhURCwCOtSr+Qjf59Upkon20SEsDMDCLDYdYxWPC7/nLffgsff5xlYeSYGgDxfEVs4NPaMGC3dsB9GgtPYrT7V312pv/aOsW0RjgT/LXX3QyHb089e752MXi1HEyup92vTusHN8ADFgTAsRDt6nLcEa2KsuxLXNWduQ+x8bDtmvbXzy11medt18Veuy/8JCbtBnYpy2RmX8oW0Kozj97VTkCLArT7z4k6lIUf/4HHMdq9zBVXnj33iot2C2bPTe29X3pROxClpUMZWHJJuz8bGglzzmU8Dhd7rQ1BSIT2GT+v/M1wrWFkhtsExMVpVyZrVsOhQxAXR4ey2j7/fkk7ya24rB30O7nq2bey2v6ERmrfsQUv0Obp06MwYj+cf6Ddaw6NhPF+0LTEy5/8QXsPd93QvichaVyVZoShfis3n2oN757EwAd/Q6tSUEJPEuFZBLZe0z6DU/e021KJOpTVqrlnnXn2/d/6r1YOoHg+/TUT/d219hPLr8DrKdpr3Hv92b31lH+JiUL38lqjVL+72m9joj80LwkOtsnXdT0cnNL47DqW1dpvnL6nNYSenqS7Z1FbrXZr1w3tKn5DkHYMSeRir30Xgx5rjRgdbNIvD9rvYf1V/bEYxLEdMKwaRD2F178AJxd4/339ZdNabiCSAOQy/6sNX9TTfkhFF0LppfDzOXitQvqvs7aADe20g1HJJeC9Tv+JclRV7UfbfrP2g03JpyRM9YY+u7RW5kFPYFmrl9undw9D4QXw3mGtgZy+dg7P2+63DbT7esUXw840+jGnLJOZfXG2g+mNoOMWcF4MB29D+STVklO8tQNKqSVaIy1nu2fPudjDPB8YuBvK/67d6yyYThuA1ypqbSFqrYH665JfwTwvjtcqQIWCUOY36Lj1+eVjE+DiIy1JeK7Ek39SVlYUsYhjSweYfgacFsF3p7VuXykP8qDdw/YoDBWXaS3tC1pnYLspdC2vnfTbbob8v4LXCoiKh7VtMr8ufSbX1RrFFVukJWIvylC/leAn2nehxGK48RSWpNHE6aMa2hV5sYUw6gBUTtJ2pLCNdgto079az6CKy+CHf7Q2KQD1i2uN4VJyttPuv1cvCm6FMx97Q2f4vC502aZ9Ny4+St1bIy4BDtyGBs761+FdXGsI+8omqLxSq0FIZGkOi1tojWaLLICll7TunIl8SmrJT5WVUGu19ltKrzxoMYZlRUey2Bj45UP4fjC8PRPyJbnnYmkJsbEpysdqy7OQmVKGqOzK+ZJOiiCTAeUMwY+h3O8QO0z7IYsc7quvYNy41MunTs2yaspFAbD3VvJxAIwlcRyAva8aOxLDOxYC3bfDlb6p2wA1+1NrEPgiPTYy4s8g+J+f1iI/T7sTDAs/gREzoFAa91jj4rRq//ffN8jJ/3nnPTnsCiEyxkjVlCLr1XHSroRnnk2+/OJDbYyAns+pYXxRsfEw7qhWs5knKQU7FsMPQ8HZFcYtS/vkD9pJ/+OPs/zKP5EkAEKIjDFSNeWSi6mHAs5OiUMBt9xonO1nlzlN4ZuTWov/RPMuaLeV8qdzy+plvLVf6zKbJ0cBfPIQpvSG1dOgyyhjR6OX3AIQQmSOgasphciTNs6Ba+fhjW/AxgCtU1/A8857kgAIIYQQhvA0TGvoV78T1NczPns2kzYAQgghRFY7sgnerKLd96/a5PnlcwCpvxNCCCFelFLa364l8MFiqJkDuqxkkNQACCGEEJmlFOxZAaPrQ0I8/G9Vrjr5g9QACCGEEJlz7ybMHAG3AmHsArDMom4SWUwSACGEECIjlIK4WHh4FyrUhP+tBqsXGM4yh5BbADlFXJw20lo2zQMthBAiE24Fwoct4Y8Z4FYLBk7M1Sd/yEEJwJYtW6hSpQoeHh5MmTIl1fOnT5/G09NT91exYkWaNWsGwKJFiyhSpIjuudq1c9mwUoljrI8bl23zQAshhMig9TNgtDfUaw/dxhg7GoPJEbcAnj59yvDhwzl69CiOjo40b96ctm3bUqtWLV2Z6tWrExDwbOqwefPmceHCBd3jfv36MWvWrGyN2yDSmGAlO0ZYE0IIkY6we9rQvVY2MP1vKKVnqtJcLEfUAPj5+VGrVi2cnZ2xtLSkR48ebNmyJc3ycXFxfP/997yfF8Yg//bbzC0XQgiRtWJj4LfJMKwqhD+CjsPy3MkfckgCcOvWLZycnHSPixUrxp07d9Isv3TpUpo2bUqpUqV0y5YtW4abmxutW7fm/PnzWRqvQckEK0IIkXPcCoSRdSDgCMzwA/vCxo4oy+SIBADA3Dx5KDEx+idkjo+P55tvvuGjjz7SLevTpw/379/n8uXLvPnmm/j6+mZlqIZlpAlWTJ40uhRCJBUdCXf/hSLFoc+nMHkTOLkYO6oslSMSAGdnZ+7du6d7HBoairOzs96yK1asoFatWlSo8Gx+ShsbG8zMzADo0aMHly9fztqADS0xCZg6VU7+2UEaXQohkvpnPwyrBtvmQz57aNYL/jun5GU5IgHw9vbG39+fkJAQ4uLiWLNmDS1btiQsLIxr167pyiUkJDB16lTGjRuX7PX79+8nMjISgLVr11KnTp1sjd8gsnkeaJOVVqNLSQKEME1LP4ev+sLQb2HQJGNHk61yRAJgb2/PzJkzad68OV5eXrRu3RofHx/Wr1/PwIEDdeXWrl1LhQoVqFKlSrLXHzp0iEqVKuHp6cnPP//M3Llzs3sXRG4hjS6FEAD/7IP4eGjQGeadhYavGjuibCfTAQvToq8GAOTWixCm4vF9mDMGzh2Cr3dDiXLGjijLyHTAQiQljS6FMF33b8PQKlDQEeaeydMn/4yQBECYHml0KYRpuX8b/LZA0RIwbS8M/wHy5Td2VEYnCYAwTdLoUoi8TynYtgDeqg6Bp7RlLh5GDSknkaOfEEKIvOn3yfD3BvhqJ1SobuxochxpBCiEECLviI+HTXOgwatgV0Dr129hmte60ghQCCGEabh2AcY2hX2rID5OG8bXRE/+GSHvjBBCiNwvOhI+6wjdx0LHt8Bcrm+fR96h3EDGrRdCiNQSEmDXUpgxHGzywS/nofMIOflnkNQA5HRJB64ZN066rQkhBMD5v2HOO4AZvPWDtszaxqgh5TaSJuVkMm593iY1O0Jk3v1bWve+m5ehyzsw/W+o3NDYUeVKkgC8jISErF2/jFufd8mMhEJkztPHMP9jGFoVbgVC64HQsp9U978Eeede1IM78IYX7F2pZaNZ4f33M7dc5A5SsyNE5ly/CEM84OFdmHcGSlU0dkR5giQAL8rBGd75GVZ9De82ghuXDL8NGbc+b5KaHSEy5sQuOLYdSlaEKdvh/YVQtKSxo8ozckwCsGXLFqpUqYKHhwdTpkzRW6ZZs2a4urri6emJp6cnX3zxBQD379+nbdu2uLu707ZtWx48eJA9QVdvBrOOQfthYF8Ebl6BawGG3YaMW5/3SM2OEOm7fhE+6wTTh2k1rBYWUL6asaPKc3LESIBPnz7Fy8uLo0eP4ujoSPPmzZk+fTq1atVKVq5Zs2Z8++231KlTJ9nywYMH4+3tzbBhw5g7dy7nzp1jxowZycpky0iAB9dpX9iGXWDARHAslTXbEblfytsAktwJobWrMjeH8Z2halN4dZS07H8JuWIkQD8/P2rVqoWzszOWlpb06NGDLVu2ZPj1u3fvplevXgD07t07U681qMbdYMElKOAAH7XShqQUQh+p2RHimdgYWPcjDK8BcbHw+Z/Q8305+WexHJEA3Lp1CycnJ93jYsWKcefOnVTlzMzM6NGjBx4eHowePZq4/xpN3b9/n8KFCwNalpNttwD0KVAE3vgafj6tVVtN6g6rvtFGqRIiKZmRUOR1GenqemoPDPHU7vWPWw6WVmBmln0xmrAckQAAmKfoyhETE5OqzNatWwkODubkyZPcvn2befPmAVpi8LzXZjsra+1f3y8h4Ci87g77Vxs3JiGEyC7pdXVVCvy2wuMHUNgJ3psPU7aCa2XjxWuCckQC4OzszL1793SPQ0NDcXZ2TlXO1tYWADs7Ozp16sSFCxcAKFKkCOHh4YB2z8PBwSEbos6gMp4wfi18tgbsCmr3uPy3ZV3XQSGEMLb0urqe2gPvNoZf3od7N7STfo3mxonTxOWIBMDb2xt/f39CQkKIi4tjzZo1tGzZkrCwMK5duwZAVFQUe/fuBSA2Npb169dTv359AFq0aMHKlSsBWLFiBS1btjTKfqSrkjfUaaP1Y53/MbzTAE7vNXZUQghhePq6tJoBX3ymjdvfcTj8/I+07DeyHNELAGDjxo18/PHHxMbG0r9/f8aPH8+iRYtYtGgRe/fuJTIykjZt2nD9+nWsra3p1KkT33zzDebm5oSGhtKvXz+Cg4NxdXXl999/p1ixYsnWny29ADIqIQH2LIcl4+H9RVClsdzzEkLkHUlrAAoAFYCnwPlYrW2UHO+yxfPOezkmAchqOSoBSBQXq81VvX0h7F8FAz7XagqEECK3i4uDGlZQFAgCLoZDvvzGjsqk5IpugCYpLg6+/U7rKtiyPzTsCl/0hE/aQlSEsaMTQogXc/YgLJ+q9W5Z+he88jlcjZWTfw4kNQDGkNYgMLExcGwbNOis3SIoWhKq+RgvTiGEyKh/9sGSCRB6HXqPg3ZvGDsikyc1ADlNeq1jray1kz+AmTl8PwTG+sDJ3dkfpxBCPE9CgnbiB7h9Fdq+AQsuysk/l5AEILtldCKYZr1gfgC0exNO7NSWBZ7O+imIhRDieeLjtVrKt6rDz+9C+CNo8zq06q+1axK5gtwCyG76agDg+cPBJiTAhy3gwW3o+QG0HCDDZAohsldsjHaC/2sZbJoDff8HddtKq/4cSnoB/CfHJADw4hPBKKWNHbDqa6hQA4Z8BTFRYG2bVZEKIYQ2lPn2Bdqw5h8ugSpNtJO+nPhzNGkDkBO96EQwZmbaiFlTtmlDDD8MgX5ltIGF7t/O2piFEKbp9lUYVF4bq//TVVrDZHNzOfnnAZIAGMvLTgRjYQFFnGDGUYgMhze94Ohmw8YoRF6XkclqTNHtIJjzLuxaCsVdYepOmLRBxinJYyQByO1KlIORs7RpiKs0hgA/+KQdHNsh8w0IkZ70JqvJqbI6YYmOhPGdYVRdbVa+6s21q/1yVbJme8KopA1AXhMTpTXQWfeDlgBMWAel3Y0dlRA5y4s2xjWmF2079DwRT2DXErC0hvZvwsF1UKct2Nq9/LqFUUkbAFNjbQttB8Pcf2D4j1DMRbt3N/9juPuvsaMTImfIaHfcnCK98UNelFLw83swoCyc+gvK/jcVb+NucvI3EZIA5FVmZlCrFdjkg9IeWs3AiFowsas2B7cQpuz99zO33NgMlbAkJGhthTb9rB0jKtaEOae0KcsrN3zpMEXuIrcATElkOBxYC60GwMG1WuveV17XGhMKYWqyqko9KxjilsWmubBmGtgV0sYSad7bsDGKHEduAYhn8tnDK4O0Rj2l3OHGRRjsDl/20moIhDAlL9od1xgSY03qeTErBZeOw+Z52mMra/hgCcw+Jid/AeSgBGDLli1UqVIFDw8PpkyZkur5qKgoWrVqRYUKFXB3d09WZtGiRRQpUgRPT088PT2pXbt2doaeO1WoDmMXwNJgaNhFazuwaS6s/hYehRo7OiGyx8t2x81OmUlYdi2F4TW0GUYf39eSgTava9X80n9f/CdHJABPnz5l+PDh7Nq1i3PnzrF161ZOnDiRqtxHH31EYGAgp0+fZuXKlZw6dUr3XL9+/QgICCAgIIDjx49nY/Q5xIt2D7IvDM37aP93rw3BZ+F1N/iqv8w7IEROk1bCEh8PflthwSfaYxs7eOtHWHQF+oyTk77QK0ekvX5+ftSqVQtnZ2cAevTowZYtW6hVq5aujK2tLa1btwYgX758VKxYkbt37xol3hwn6f3BceNevDrTvQ58sAiePISzB7RbBb9NgoR4aD0ISpQ3aNhCCAPYPA9+n6RNH95miHa136S7saMSuUCOqAG4desWTk7PGqIVK1aMO3fupFn+7t27HDlyBG/vZ6NSLVu2DDc3N1q3bs358+ezNN4cJSu6BxUo8mxa4gavajN9jfaGzzpqBxfTaDcqRM4U+RR2LoGpfbVaujKV4MutMNMPOg6Tq32RYTkiAQAwN08eSkxMjN5y0dHR9OzZky+//JLChQsD0KdPH+7fv8/ly5d588038fX1zdJYH0XDmkBYdxWe6A8z+2R1f+YK1WHEdFh2EwZO0g4u3wyEST1g/2qIijDMdoQQaYv970Czfgb0d4H9q6BJDy0Zr9oEylXNum3LcMl5Vo7oBrh7925+/vlnVq9eDcD06dO5f/8+kyZNSlYuJiaGbt260ahRI8aNG6d3XQkJCRQtWpSHDx8mW26oboBH70L7zfAgWnvsbAc7O0KVoi+8ypdjjBHNHj+AQ+th30pteuJ5Z+BOMDiUkCmKhTCUkGvw9wbt73qA1mD35mWwK6BV92eH3NRVUqSSK7oBent74+/vT0hICHFxcaxZs4aWLVsSFhbGtWvXAIiIiKBTp040adIk1cl///79REZGArB27Vrq1KmTJXEmKBiw+9nJH+BOBAzemyWby5gX6R70sgo6QLsh8NUOmOWvLVv/I/QpAd8MAr8tcptAiMxK7La3Y7H2+M9ZcOkYdHwLfjmntclx8TDeyR9yz5wJIkNyRAJgb2/PzJkzad68OV5eXrRu3RofHx/Wr1/PwIEDAa2h4N69e1m4cKGuu19iInDo0CEqVaqEp6cnP//8M3Pnzs2SOO9FwuWw1Mv9QyAmPks2mTHG7M9sbav9O/xHmHcW3GpptwbMzLQrlxO7IF4OGELolZgoL/sS+rnA1D5w85K27M1vtEa5jbtpV/3ZLbcNlywyLUfcAsgOhrgFEBUHRRZAVIqTfVFbCPWVtjep7FwCf87UqjJ9emltCeRNEqYu7J5WS/b3Bnj6CL7eBaf3arfQXDyMHZ0mLg6+/hr+97/Uz8ltgFzjeec9+RQzwdYSPqoJnx9Lvvyz2nJe06v1QO3v5hUIOKq9SUsmwqMQbfCh6s200cmEyOtuXIL7t7Tv/Ff9tFE563eGeu2156s3M2Z0yaXVrgjk5J/HSA1AJikFCwNgxRWwMINBHtDbzRARmogbl7QGhH//CaE3tIZNd//VDoiFixk7OiEMa/F42LcKIh5r9/L7j9cOIjn1iiGtk/+XX8KHH8rJP5eRGgADMzODwZW0P/ECSrtDr4+0v6ePtYZNh/+A3z7X+jN7d4Tu72mzGAqRm0SGw/GdcGQDFCwKQ7+FkhXh49+gYi3tuw459+QPad/fNzeXk38eJDUAImeIidZGHzy5G17/UhvL/Ph2qN4carTQRiHMyQdOYZru39IS2dLuMKg8lHTTBtFq0BmKlzV2dJlnjG7FIss877wnCYDIme7dBP9tcHoPnPoLfjioDUl87rCWEDi5PH8dcXHaFc3778vBSxhOTDSs+ka70r8dCH0/g+7vaoP15IU2LdL3P8+QBOA/kgDkYolf0cBTsGKq1mI6f0H46HeoWBOePAAH5+SvkYOYMJTYGDizX2u1X6GGNqvekgla7VSVxmCZRoO53EyS5zxBEoD/SAKQhyQkwL/noJgLhF6HsU21LlQ1WkC7N6BsFanGFC8uKgIu+mnfr0KO0L8suHhq1fpNe0IpafUrcgdpBCjyHnPzZ2Of2xeG1fe02oFTf0F0JHz1BXgDD4FH//3FoF3RfPyxUUIWOdjDu5C/ENy+Ct++rk2JXb4aDPgcSlaAxYFaoz4h8hipARB5T3QUFM8HRYDCaAlAMDBrFDiVAY962oiF+eyNGKTIMulVX0c+hXz54cIRbajdgCPw+D5M3QGlPbRE0qOu9EIReYLcAviPJAA5RHbdW9TXBmD/Km1AooCjWtXu5E2waS5ER2gJQYUa2pWgyL2Sfu5mQEws+G/R+uJfPq6NSrniljb2xEV/qFRf635qniNGRRfCoCQB+E9WJQCRcbDsMlwJg6oO8FpFsJRjiX7Z3TAvvWQjcTCWQ3/AyV1w5QSEXIffr8GRTVqSUNZL+yvjlTdad+dVTx9D1FMoVAwqWGm1PvaAAv4GDm+AsFBwq619nnmx0Z4QekgC8J+sSACexkKzP+FY6LNlHcrCn23BQpKA5HJD/+LEpOCiPxzdDNcvwL/nYcJ6bcz2pZ8/SwhqNNduJ4jskZCgNfi8flFrhFekOHzWEW5chPBH0PltuO8AM8ZBAvD0vz+FNkmWtP0QJihLE4AlS5boZuvL6bIiAfjuFLz/d+rlq1+BHhUMsom846uvIMU0zkDuOTg/fgD/7INr57WkoNUAqNoU3vTS7h2XcoOaraBRF7gTDAUctK6KInOiIrST+vWLcD0AuoyG4DPwaXutwaeLJ/QeBzVbajNNuniAY2mtCj83JJnCJDyMhrgEKGbkpiQGSQDOnz+faplSiqFDh3Lo0KGXDFGzZcsWPvzwQ2JjYxk0aBCffPJJhsvcv3+ffv36cfXqVcqXL8+yZctwcHBI9tqsSAAG79HmBUhpYh2YUNcgm8g78uLBOSEB7gZrCcHtQO1E1KQ7TOquDWJkawdudWDKVm2I2KAzUKw0OJbShoY1tYZmMVFai3vQRsnbsVi79RJ6XXvuyy2wfArsWa6d6F084dVRWiIVG5OxhErGf8iVYuLhSSw42OTuAT8fx4DvX7A+SHvctAQsawWljNTe2CAJQMGCBenRowcpi+7cuZMbN268dJBPnz7Fy8uLo0eP4ujoSPPmzZk+fTq1atXKUJnBgwfj7e3NsGHDmDt3LufOnWPGjBnJtpEVCcBXJ2Dc0dTLl7WCPtJVODVTOjgrpc16+ChE67J4fCcc3aSNcHj/JnyyQut2NrGL1sWsgAP0+hia9oC5Y7UeCgWKaifBOq9oZRMStLL5C+WMRmvhj7R76+GPtL/qzbQ49yz/b9/vQqcR4OkNfUpBbBQUcoKW/WHIVNj0s3bF7+QCTmXBs55h4pJBbHINpbTZVb85pbWnqlQElraE2rl0XrCBu2HppeTLmpSA/V2MEo5hEgBvb2+2bNlC0aLJ+8J26NCBzZs3v3SQe/bsYcaMGaxfvx6A6dOn8+TJE/6XZC7q9MqULVuW06dPU7hwYcLCwqhduzZXrlxJto2sSADCoqHhejj/8NmyZiVhR0ewsjDIJvIeOTg/k5CgzRL3+L72V7SkVjvw5yzt8ZP7UNwVeoyFOWO0keiePNB6LWyMhL0rYNH/wMoGrG1gxAxwrQJf9tKWWdlAnbbQYah2Zf3gNphbagnEwIlaLcXRTdrVd0wU+H4BsdEw6+1ny9q9CR2HwVs14N4NbVn5avDjYfj5PW043PyFter5/63Wyuxbpd2jL1IcKjXQ9in8kVYmN1/eCYP79Ty8uS/5MkdbCOwHBXNZu1ulwO4XiIpP/dztQeBsl/0xvdRAQPHx8VhYWLBz507y58+f6nlDnPwBbt26hZOTk+5xsWLFuHz5cobL3L9/n8KFCwPaTj548MAgcT1PIRv4uxvMvwCXw6BaUXjdU07+6bK0zB33/LODubl2UrQvrA04k6jLqNRlh/+o/QHEx4GFBTR8FbwaaCft2GgtWbC21WoSEpclNlQs5Qb5CkBcLNj+91vOX0hrv2CTD6xswa6gtt5eH2vrsbbVbmuAVj1vYfVsOcBb32t/SRUo8myQppTLcxJJRHOE5VdSL7sXBX/dhC7lsj+el2VpDuhJACxzaN6b7je/bdu2rF+/noIFs74xk3mKKs2YmJgMlzFLcVWh77VZpaA1vFs92zYnBFj897PNZ69/MKNarVIva9oz9TKvBtpfRl5ftGTmYszJkt6KGjcub9+KyuHM0zgx5oAbXJlmZga+HjDrbPLlHcuCYw5t7pPu+1yjRg0aNWrErVu3dMv2799PkyZNDBqEs7Mz9+7d0z0ODQ3F2dk5w2WKFClCeHg4oFV5pGwAKIQQgP7GqFZW2nKR7Qa4p15Wwg5als7+WAzh24YwsgrYWYK1OfSpqLVpyKnSTQCmTZvG8OHDadSoEStWrKB169a89tprdOnSxaBBeHt74+/vT0hICHFxcaxZs4aWLVsSFhbGtWvX0i0D0KJFC1auXAnAihUrdMuFECKZb7/N3HKRpQa4w7QGUPi/+/11imltqPLn0rGabCxgZhMIfwMih8Ky1lDYxthRpe25jQD/+ecfBgwYwNmzZxk0aBCzZ88mXz7D12ds3LiRjz/+mNjYWPr378/48eNZtGgRixYtYu/evWmWAa02oF+/fgQHB+Pq6srvv/9OsWLJm5HKUMBCiDzZHTUPUAqi48FWPgKDeqleAN26dWPv3r2MGTOG6tWrM2LECObNm0eHDh2yJtosJAmAEAIwre6owqS9VC8ANzc3fv31V9099fLly9O5c2eCg4N5++23DRyqEEJkA0tL7aQvvQCEicv0UMA3b96kQ4cOnDp1KotCyhpSAyCEEMLYEhQsCtC6OjrYwvDK2gBIWSFL5gJ48uQJBQoUeLnIspkkAEJkA+lfL0S63tgD85MMIW9nCftehTpOab/mRclsgP+RBECILCb31oVIV8BDqLQi9fKOZWFje8Nv73nnvdw43oIQIqfJaP/6uDhtZkjpdy9MUPAT/cuvPs7eOBJJApBHRcfDgVtw9C7EJxg7GpHnZaR/fWKSMG6cDL4jTFJVB7DQM/phTcfsjwXkFkCedOoedNoCN55qj6s6wNYOxpuSUhiXUjDjjPYXFgPtysCPjaCorQE38rz+9dL/XggAfjgN7x1+9rhsATjQBVyy4PgstwBMTIKCXjuenfwBzjyAYfuNF5Mwrp/OwZhDWjXj/Sj47RJ02aolBgaT2LUuqaQndxmBTwhAmzvmWA9tBMT5zeB0z6w5+WeE1ADkMf8+AdffUi+3sYCoodkfjzC+Kivg3MPUy8+8BlWKpl7+UtLqBSA1AEJkO6kBMDEFrfXPsFU4l82tLQwnIo1b7WktfymJ0z2nPKk/r4ZACJHtJAHIY4rYaFNSpvSeTFlssjq7pl5WOj/UyO6GR4lJwNSpcvIXIgeQBCAPmtMUJtSBykW01qWzmsAHNV5unXEJ8OlRKLkYii+CUQcgUhpx5wpfekPXcs8euxaAP9uBtTJCl7y0aghyKum2KPIwaQMgMuT9w/Dd6eTL+rnBb62ME4/IvOvh8DgGPAqDZYIM2vNcMrCRyOVyfBuAY8eOUbNmTdzd3Rk9ejQJCfo7rb/22muUL18ed3d3Ro0aRWLesnfvXuzt7fH09NT9PXr0KBv3IO9TCuaeT7182WUIi87+eMSLcbGHyg56Tv4g/fJTyujARkLkYkZPAPr168dvv/3GpUuXCA0N5Y8//tBbbsCAAQQGBnLhwgWuXLnChg0bdM81a9aMgIAA3V/hwoWzJ3gTodBf3a+AGBlkKPeRLnnPJ++RMAFGTQCCgoKws7OjcuXKAPTu3ZstW7boLdupUyfMzMywsLDAy8uLO3fuZGeoJs3cTBurOqX6xcHRkIPJiOzx/vuZW26K5D0SJsCoCcCtW7dwcno2BVKxYsWee2KPiIjgzz//pFmzZrpl+/bto2LFijRq1IiDBw9mVbgm7Zdm0KTEs8c1HGF5KzDT0+VQ5HDSJe/55D0SJiBbvs2tWrXi3r17qZbPnj0bc/PkOUhMTEya61FKMXjwYAYMGICHh9bXrVGjRoSFhWFubs6hQ4fo1asX169fx0zOTDqXHsE/96F8Qa1XwIu8NcXyaVNWBj3RegS4FZKTf66WeIKTqXvTJu+RyOOy5Ru9a9cuvcsDAwOTJQahoaE4OzvrLauUYtiwYRQuXJgJEybollslaajTqFEjrK2tefDgAUWLGnqIs9xpoj98fuzZ475usKQFWLxA3Y+ZmZZEiDwisUueSJu8RyIPM+otgAoVKhAWFkZAQAAAK1asoGXLloBW1R8YGAhAfHw8vr6+WFtbM2fOnGTrOHr0qK7V/5EjR7C2tpaT/38O3U5+8get5f7CAOPEI4QQhhYTD5/5QaXlUG0lTP/HwPNc5GFGr9P67bff6N27N0+fPuWVV16hf//+APj5+eHr60twcDDXr19n6dKluLu7U6lSJQDq1avHkiVLuHDhAn379sXCwgIHBwd+//13Y+5OjrL/dtrL3/DK3liEECIrDN8PC5Jc1Iw5BE9j4ZPaxospt5CBgPKwBRdgyN7Uy9+vDtMaZnc0pmVDEHxzCkIjwackfFUfHKTHhBAG9Sgaii7UZkFNytEWQl83Tkw5yfPOe0avARBZp2cFmHICAh8/W1bIGoZXMV5MpmDLv/DqtmePL4XB6ftwuOuLtb0wtJAImHVWmx64hiOMqAx2eibqEyKnexKb+uQPEJZ2W3KRhCQAeVgBazjQBSYeg1P3oEJB+F9taciX1X78J/UyvxA4fAealMz+eJIKiYB667RpowF+vwzrg2BPZ7C2MG5sQmRW6fxQqQhcSDHd9Ssuxoknt5EEII8rkR/m+hg7CtPyKI2rj7SWZ6dZZ5+d/BMdvqMlAb0qGicmIV6UmRmsbA2dtj77XlcvCvPkmJchkgAIYWCtS4N/SPJldpbQoLhx4knq6uPMLRcip6taFC71gWOhYG0OtYppo5eK58sBdySFyFv+Vxs6JBk62d5KGzXRMZ/xYkpUwzGN5dJzVuRi1hbQ0BnqOMnJPzOkF4AQWUApOPcAQqOgliMUsjF2RJqIWGi9Sav2TzTAHRa3kJEdhchrnnfekwRACBMTEw9/BGm9Q2oUhbZl5OQvRF4k3QCFEMlYW8Br0uBPCJMnbQCEEEIIEyQJgBBCCGGC5BaAyHZKwfbrsP+WNs3wAPec0UJeCKG5GQ7D9sPO61DYBt6tBh/VlLYieY00AhTZ7sO/YdqpZ49L5YdDXaFsAaOFJIT4T3wC1FoD/9xPvnxGYxhV1TgxiRfzvPOe3AIQ2SrgYfKTP8DNpzDR3yjhCCFSOB6a+uQPyWfcE3mD0ROAY8eOUbNmTdzd3Rk9ejQJCQl6y/n6+lKqVCk8PT3x9PRk+PDhAERGRtKnTx/c3d1p2LAhQUFB2Rm+yKTzD/UvP5fGciFE9orRfwgmJj574xBZz+htAPr168e6deuoXLkyffr04Y8//qBbt256y06fPp0ePXokWzZt2jRcXV1Zvnw527dv55133mHDhg3ZEbp4Ae5p3H1Ja7kQInvVdQIXe7gennx59/LGiedlKKV48OBBmheWeYm5uTkODg6YZaKhhlETgKCgIOzs7KhcuTIAvXv3ZuPGjWkmAPrs3r2bGTNmANCmTRsGDRqEUipTb4LIPlWKwjAvmHv+2TJHW5hQx3gxCSGesbGATe2h1w4IeKQNrevroQ1xnds8ePCA/PnzY2tra+xQslxUVBQPHjygaNGMj+tt1ATg1q1bODk56R4XK1aMO3fu6C1rZmbGmDFjGDduHPXr1+enn36iQIECqdZRsGBB7t+/j6NjGoOeC6Ob0xRalIL9t8EpHwzxhFL2xo5KCJGoWlE41xtuhGvTihfJIUNZZ1ZCQoJJnPwBbG1tefLkyfMLJpEtCUCrVq24d+9equWzZ8/G3Dx5M4SYGP1zps6ZMwdbW1tiY2N5//33mTx5Mt988w1AhtchcgYzM20kOhmNToicy9wMykjPnDwtWxKAXbt26V0eGBiYLDEIDQ3F2dlZb9nELM7Kyoru3bszbdo0AJydnbl37x7Fi2tzrT569IhixYoZMnwhhBAizzFqL4AKFSoQFhZGQIDWv2TFihW0bNkSgIiICAIDA3Vld+zYgVIKpRSrV6+mfv36ALRs2ZIVK1YAsH37dipXroyVlVU274kQQgiR2s2bN3XntaRcXV311ow/T3BwMFWqVDFEaMbvBvjbb7/Ru3dv3NzccHBwoH///gD4+fkle9N+/vlnXF1d8fT0JDw8nHfffReADz/8kIsXL+Lm5sbEiRP55ZdfjLIfQgghREqlSpVi9+7dxg5DLxkJUAghRJ4UGhpq9FvC9+7do06dOgQGBjJ69Gi2b99O0aJFOXfuHMHBwTg6OrJhwwYmT55MeHg4NWvWZOHChdjY2FCtWjWioqJQSvHWW28xduxYgoOD6dixI2fPnk21rZT7KyMBCpGHHLgFDdZB0QXQ7E84EWrsiIQQGTF//nwePXrE5cuXOXToEIULFwbgxo0bzJgxg/3793PhwgUcHR1ZvHgxoLWfu3TpEidPnuTrr78mIiLCoDEZfSAgIUTGnH8Ar2yCqP9GZNt3C1psgLO9oLR0oxTi+ZZMhN8+f/Z41jHt35FJBiLpPwEGToTeJeHBbW1ZxVrw03H4YShsTXKbeflNKFoyQ5vesWMHw4cPx8zMDEtLSywttdPv3r17OXXqFDVr1gQgOjqaggULAlq7uFWrVvHgwQPCwsIIDTVsxi8JgBC5xOKLz07+icJiYPll+KCmcWISIlcZOFH7S2mHnjvhK26lXvbuPO3vBURHR+ttoJ6QkEDPnj2ZM2dOsuVLly5l5cqVrFixAhcXF6pUqYKh79jLLQAhcomncZlbLoTIOerUqcOmTZsAuHr1Ko8fPwagSZMmbNiwQdfr7c6dO5w6dYr79+/j7e2Ni4sL//777wv1GHgeqQEQIpfoWBZmp273Q4ey2R+LECJzRo8eTa9evfD09KRy5cpYWFgAUK5cOWbNmkW3bt2IiIjAysqKmTNn0r9/fzp27Ii7uzteXl5Z0r1degEIkYtMPQET/CE2AfJZwvcN4a3Kxo5K5FSx8dp02075wM4Eh0fJCb0AslNmewFIDYAQuci4WtpkSv8+gQqFoKC1sSMSOdXGYHhjL4REasnihDrwkbQVEUlIAiBELuNgq/0JkZagx9BzB0T/12g0Mg4+PgIehaFLOaOGJnIQaQQohBB5zKZ/n538k1oTmHqZMF2SAAghRB5jncaR3doie+MQOZskAEIIkcd0KQdFbJIvMwN8PYwSjsihJAEQQog8prgd7OwEdYqBuRmULwgrWkPTjA1aJ0yENAIUQog8qHYx8O9h7CjEyzhx4gS+vr78888/WbJ+qQEQQgghcpixY8fSunVrEhISsmwbRk8Ajh07Rs2aNXF3d2f06NF6d/bevXt4enom+ytXTuvLsnfvXuzt7ZM99+jRo2zeCyGEEHlCXBx89ZX2rwF89tlnfPHFF7rHH374IbNmzXru67777juOHz9ukBjSYvRbAP369WPdunVUrlyZPn368Mcff9CtW7dkZRwdHQkICNA93rFjR7KJE5o1a6YbY1kIIYR4IXFxkDjk7rhxEBsLli93mhwyZAitWrVi3CefkhAfx+rVqzl58iQNGjTg4cOHycqWLVuW7du3v9T2MsOoCUBQUBB2dnZUrqyNZdq7d282btyYKgFIafLkyXz//ffZEWKeoxTEJYCVdAcSQohnkp78E1lZvVQSEJcAcUVcKVqqHPPW70FFPqJpUx8KFy7M33//bYCgX45RbwHcunULJycn3eNixYpx586ddF+zZ88e8uXLR926dXXL9u3bR8WKFWnUqBEHDx7MsnhzM6Xg65NQdCFYz4Mm6+HyI2NHJYQQOcS332ZueQYEP4FH0fBqvzf4c9l8fl/0Kx37DwWgQYMGqW5tt2nT5oW39SKypQagVatWeqcynD17NubmyXOQmJiYdNc1efJkxo8fr3vcqFEjwsLCMDc359ChQ/Tq1Yvr169jZmZmmODziPkXtKFAEx28A69sgoA+YCO1AVkuQcHVx2BrAaXtjR2NECKV99/Xqv31LX8B8QnayR+gefuuTJ84lkJFilKuekMSFDmiBiBbEoBdu3bpXR4YGJgsMQgNDcXZ2TnN9Rw6dIjo6GiaNWumW5Z0isRGjRphbW3NgwcPKFq06MsHnocsvph6WfAT2H8LWrtkfzym5OJD6LYdzv93u69DWVjWSibyESJHsbTUqvuT3gYwQBsAACtra+o0bkGl6nUy/Jrx48fzxx9/EBgYSJ06dfjuu+/w8fF56ViSMuotgAoVKhAWFqZr4LdixQpatmwJQEREBIGByQeunjx5Mp988kmyZUePHtW1+j9y5AjW1tZy8tcjLo1Jn+NNYjJo40lQyU/+AJv/hXcPGS8mIUQaEpOAqVNf+uRvYQ6F/xuNMS42ltN+h2jfcwAOttrgTM8zadIk/vnnHyIjIzl27JjBT/6QA7oB/vbbb/Tu3Rs3NzccHBzo378/AH5+frpkAMDf3587d+7QoUOHZK+/cOECtWvXxt3dnTFjxvD7779na/y5Ra8KqZeVsIOmJbI/FlNyJSz5yT/RH0HZH0tWuPYEJvjByAPaRDNKEkqRAQlK+22ERBg7Ej0sLeHjjw1y5e9aQEsC/tq8jur1GuHqXISyOegWoJlSpvGTDQsL0/2/UKFCRozEOBKU1gZg+j8QkwCVimjV0DUcjR1Z3nbtCZT9LfXyUvnhxsDsj8eQLjyERuvhYfSzZSOrwMwmxotJ5Hyn70GPHVoCANCnIixoDrZZcEM6NDSUYsWKGX7FmZTw31k2I1f+LyPl/j7vvGf0GgCRPczN4JsG8GgI3B0E53rJyT87lCkAbfW0sRjmlf2xGNoE/+Qnf4BZZ7U2D0LoExMPnbY+O/kDLL+ifZfyMnOzrD/5vwhJAExMPktwsoO0OkkoBTP+gYq/Q/FF8PpfqQ/yInNWtNZmYStiAyXzw+d14dPaxo3J7y68d0j787v7Yuu49CiN5WH6lwtxIhSuh6devj6P3BLLbYw+EqDIWWafhXeSNFBbdFHrLfBX57STBpG+QjawsIWxo3hmxWXouwsS7/1NPwO/tYQ+bplbT2UHOH0/9XKvIi8dosij0qrml67IxiE1ACKZn86lXrb3FgQ8yvZQRBZIUDDm0LOTf+Kydw9lvgHf5HpaQ9KkPq0FFUyviY3IoGpFoa5T6uVD88AtsdxIEgCRzNPYzC3PDWLi4U6ENjCHqXsSA3cjUy+/GwlPMvkZly8Ip1+DHxpqJ/6dneALb8PEKfImczPY1A66lwd7K60x7Df1tcajIrktW7ZQpUoVPDw8mDJlSpZsQ24BiGQ6uWq3AZJysYfq2TS0wuMYrZ98ZBy84vLyo+bNOgOf+mnrLZkffvGB9mUNE2tuVNBa+zxT3od1sYcCVvpfk55i+WBMdcPEJkyDkx2syd4RbzPlZjjciQTPwpD/BX4ThvD06VOGDx/O0aNHcXR0pHnz5rRt25ZatWoZdDtSAyCS+ao+dExygixbAP5omz2TBwU8hEortPvTQ/aC2zItGXhR267BqIPayR/g1lNtUJ6rjw0Rbe5kZgY/NQGrJL98K3NtmbTxEKYsPgGG7YPSS6HOGii5xDDjdbzIdMB+fn7UqlULZ2dnLC0t6dGjB1u2bHn5YFKQGgCRjL0VbGwPQY+1KmGvImCZTWniyAPaSTpRVDz4/qX1l3+RRkKrAlMvi46HDUGmfdXa0VWrul/z3/vTo4I2LoQQpmzOOZh3/tnjxzHQeycE9oVSL1ETmTgd8KeffkpcXMamA9Y3Ud7ly5dfPIg0SAIg9CpXMPu3eURPd7R7UdoV+4ucoCzSuKK1MFBCcz8KxvvB0RAonR/G1QLv4s9/XUw8LLkI/zyA8gVgcKXsnxegUhH4LOPDkme5h9Gw7DKERoJPSWheytgRCVOjr7YxOh5234SBHi++XldXV8qVK8eePXt49OgRPj4Zmw44sxPlvQhJAESO4WKfureBpTkUz/di6xvgDr9eSL7M3gq6lnux9SUVHQ8tNsA//3WDOx4K267D312hZjoDj8UlQMctsPPGs2W/XIBDXZ+NG25qrodrIwombZfwv9paLwMhsot9Gvf701qeGW+88Qbz58/n4cOH/O9//wNItwbA2dk5UxPlvShpAyByjM/rpl72XjVwsH2x9TUtCb+31K7OASoXgW0d0m5YGBMPB27BwdsQG5/+urdee3byTxQdDz/+k/7r/ghKfvIHba6An87qL28KxvulbpT4xXG4/Mgo4QgTNbwypKw0LF9Q/0iemdW1a1f27dvHzZs3adiwIaBNBxwQEJDsb/v27QB4e3vj7+9PSEgIcXFxrFmzJtncOIYiNQAix3itIhS11e7DRcVDl3LaCHovo6+79hcbn35DxoCH0H4zBD3RHlcsBFs7aP/qE6qnKx1otyzSk9Z4ChfTWG4KzjzQv/zsA3ArnK2hCBPWojSsawuTj8HNp9DAGX5sBHYGqAGwtramRYsW1KmTsftu9vb2zJw5k+bNmxMbG0v//v2zZDZASQBEjtKytPZnaOmd/JWC/rufnfxBG6vc9y842FX/a5qU0K4WUo6d41My/TjSGiXPyyH91+VlboW0Wyj6lguRnbqU0/4MLTY2lkOHDjF9+vQMv6ZTp0506tTJ8MEkkSNuAQQHB1OqVPqtfo4dO0bNmjVxd3dn9OjRJCRoo7pERkbSp08f3N3dadiwIUFBMqi0yJywGP0noEN3tPEI9PEsArOaJG9o2NkVxlRLf1uvukKHFOMQVC8KIypnJuK8ZXwdbZ6EpIZ4QpVsGntCiKy2bt06GjVqRJEiOau7jdGnA/7hhx+YOnUqERERhIfrmSXiPx4eHqxbt47KlSvTp08fevbsSbdu3Zg0aRKRkZFMnTqV7du3M3v2bDZs2JDq9aY+HbBIW3Q8FJqv/ZtUfksIG5J+r4Hr4doEJ6XtoZZjxvrSxyXAyivaOPrlC2qNFY014EhO8e8T+OU8hPzXC6CPW86cPU3kLjllOuDsktnpgI2eACSyt7dPMwEICgqiW7dunDx5EoA///yTjRs38uuvv+Lj48OMGTOoXl3r2O3s7Mzt27cxS3EklgRApOf9w/Dd6eTLPq0lQ9sKkZtJApD+eS9XtAHQNyjCnTt39D5XsGBB7t+/j6OjTHYvMu7r+uCUT+uLbm4G/d2fX50vhMjZzM3NiYqKwtb2BbsS5SJRUVGpxg54nmxJAFq1apWsT2OiLVu2ULLkc1pN/Se9QRGyY8AEkbdZmMOHNbU/IUTe4ODgwIMHD3jy5MnzC+dy5ubmODhkrjVxtiQAu3bteqnXpzcoQuJzxYtrQ7A9evTIpKp8hBBC6GdmZkbRotKaNC05oheAPhEREQQGaoOVV6hQgbCwMAICAgBYsWKFblCEli1bsmLFCgC2b99O5cqVsbIy8RZVQgghxHMYPQGYM2cOderUITIykjp16rB8+XJAmw0p6chHv/32G71798bNzQ0HBwf69+8PaDMrXbx4ETc3NyZOnMgvv/xilP0QQgghcpMc0wsgqyVtDSmEEEKYEn29AIxeAyCEEEKI7CcJgBBCCGGCTOYWgBBCCCGekRoAIYQQwgRJAiCEEEKYIEkA0pHWDIRJ3bt3D09Pz2R/5cpp80nu3bsXe3v7ZM89evQom/fi+TKynwC+vr6UKlVKty/Dhw8HcteMjBnd19dee43y5cvj7u7OqFGjSLxTltM/0y1btlClShU8PDyYMmVKpsrcv3+ftm3b4u7uTtu2bXnw4EF2hZ1pz9vPqKgoWrVqRYUKFXB3d09WZtGiRRQpUkT3+dWuXTs7Q8+0jHymzZo1w9XVVbdPX3zxBZC3PtPTp08n+91VrFiRZs2aAbnvMwU4ceIE1aqlPd54tvxOlUiTu7u7Onv2rFJKqd69e6u1a9c+9zXbt29XXbp0UUoptWfPHtWhQ4csjdEQMrqfgwYNUqtXr061/PPPP1cff/yxUkqpbdu2qU6dOmVdsC8po/u6YcMGlZCQoOLi4lTbtm3VH3/8oZTK2Z9peHi4KlOmjLp9+7aKjY1VjRs3VsePH89wmddff139/PPPSimlfv75ZzVq1Khs34eMyMh+RkZGqh07diillIqIiFDVqlVTJ0+eVEoptXDhQvX2229nd9gvJCP7qpRSPj4+yt/fP9XyvPSZpjR37lw1ZswYpVTu+kyVUuq9995TDg4OqnLlynqfz67fqdQApCEoKAg7OzsqV9Ymau/duzdbtmx57usmT57MJ598ktXhGcyL7mdSu3fvpnfv3gC0adMGPz8/3RVzTpKZfe3UqRNmZmZYWFjg5eWlm3wqJ/Pz86NWrVo4OztjaWlJjx49Uu1femV2795Nr169gBf7HmSXjOynra0trVu3BiBfvnxUrFiRu3fvGiPcl5KRfU1PXvpMk4qLi+P777/n/fffz8YoDee7777j+PHjaT6fXb9TSQDSkN4MhGnZs2cP+fLlo27durpl+/bto2LFijRq1IiDBw9mWbwvKjP7aWZmxpgxY3Bzc2PAgAG6CTbSmpExp3mRzzQiIoI///xTV9UIOfczzcj+pVfm/v37FC5cGNAGDcmp1cWZ/Rzv3r3LkSNH8PZ+NrfzsmXLcHNzo3Xr1pw/fz5L430ZGd1XMzMzevTogYeHB6NHjyYuLg7Iu5/p0qVLadq0KaVKldItyy2faUZk1+80V0wHnJXSmqlw9uzZmZ5lcPLkyYwfP173uFGjRoSFhWFubs6hQ4fo1asX169fx8zMzDDBZ4Ih9nPOnDnY2toSGxvL+++/z+TJk/nmm2+AnDUjo6E+U6UUgwcPZsCAAXh4eAA56zPVJyP7l1aZlPuQk2fVzOjnGB0dTc+ePfnyyy91B80+ffowaNAgzMzMWLVqFb6+vvj5+WV1yC8sI/u6detWbG1tiYiIYNCgQcybN48RI0bkyc80Pj6eb775hk2bNumW5bbPNCOy43dq8glAWjMVBgYGpjkDoT6HDh0iOjo62ZVi0kmJGjVqhLW1NQ8ePDDK7FSG2M/EObWtrKzo3r0706ZNA3LejIyG2FelFMOGDaNw4cJMmDBBtzwnfaYppTdrZkbKFClShPDwcOzt7QkLC8v01KLZJSP7CdqBsXv37rRr1w5fX1/dchsbG93/e/TowbBhw7I03peR0X1N/G3a2dnRqVMn/P39gbz3mYI2GVytWrWoUKGCbllu+kwzIrt+p3ILIA3pzUCYdKbCRPru/R89elTXQvzIkSNYW1vniBNFUpnZzx07dqCUQinF6tWrqV+/PpB7ZmTM6L7Gx8fj6+uLtbU1c+bMSbaOnPyZent74+/vT0hICHFxcaxZs4aWLVsSFhbGtWvX0i0D0KJFC1auXAkkf29ymozsZ0REBJ06daJJkyaMGzcu2ev3799PZGQkAGvXrqVOnTrZvg8ZlZF9jYqKYu/evQDExsayfv163W8zL32mAAkJCUydOjVXf6ZpMcrv9IWbD5qAo0ePqurVq6uKFSuqESNGqLi4OKWU1hK8bNmyunJ+fn6qevXqqV6/cOFCVb58eeXm5qa8vb3VsWPHsinyzMnofnbt2lWVKVNGubu7K19fX/X06VOllFJPnz5VPXv2VBUrVlT169dXly9fNsZuZEhG9jUoKEiZmZkpDw8P3d+AAQOUUjn/M92wYYPy8vJSbm5u6vPPP1dKaTH7+PikW0YppUJCQlTr1q2Vm5ubat26tQoJCcnu8DPsefu5Z88eZW1tnewzTOypMmXKFFW2bFnl4eGhWrRooQIDA421GxnyvH2NiIhQTZo0Ua6ursrd3V2NHTtWxcfHK6Xy1meqlFKrVq1SnTt3TvXa3PaZfvbZZ6pq1arK1tZW1a5dW+3du9cov1MZClgIIYQwQXILQAghhDBBkgAIIYQQJkgSACGEEMIESQIghBBCmCBJAIQQQggTJAmAEEIIYYIkARBCGNSHH35I165ddY8/+OADWrZsSWxsrBGjEkKkJOMACCEM6v79+1SoUIG9e/dy5MgRZs+ezcGDBylUqJCxQxNCJGHycwEIIQyraNGijBkzhoEDBxIWFpbs5P/pp5+yf/9+ihcvzpIlS7CzszNytEKYLrkFIIQwuJo1a3LmzBmmTp2Ki4sLAGfPniUwMJADBw7QqlUrFixYYOQohTBtkgAIIQzqzJkzDB8+nEGDBiU7yR84cIB27doB0K5dOw4ePGisEIUQSAIghDCgmzdv0qlTJ37++Wd++uknzpw5o5up7uHDh7pbAYUKFeLBgwdGjFQIIQmAEMIgHj9+TPv27Xnvvffo3LkzdnZ2fPDBB3z66aeANo95WFgYQI6em14IUyG9AIQQ2SKxTcCyZcuYN28e0dHRjBo1ythhCWGypBeAECJbVK1albJly9KkSROcnJxYsmSJsUMSwqTJLQAgKiqK9u3b4+Pjw9mzZ7N8e6tXr2bXrl3Ztt6AgAD69OkDwJEjR5gwYYLBt51b/fDDDyxevFj3+N69e/Tt29eIEeVtU6dO5cCBA6xdu5b8+fMbOxwhTJokAMDmzZupV68e+/bto0qVKlm+vZ49e9KqVatsW+8///xDzZo1Afj66695++23Db7ttPj6+hIcHJxt28vs9k+dOkX16tV1jx0dHSlQoACnT5/OhuiEEMJ4TD4BOH78OO+88w5//vknX3zxhUHXrZRi1KhRNGvWjMaNG7Nq1SoA6tWrR2xsLKtWraJBgwa0bt2avn37snv3bkDrQ/3ZZ5/RoEEDhgwZwoIFC2jWrBmNGjXSDae6YsUKvL298fb25uuvv062XoD169dTv359OnbsyKZNm6hRowZPnjzh3r17ODk5cezYMV0iMHjwYN292L59+3Lx4sVU+5KQkIC3tzfXrl0jODgYHx8fQkJCXvi9Mfb2E/3zzz8sXryYZs2a0blzZ+Lj42nfvj2rV69+6XULIUROZvJtAGrXrk2tWrWYPXu2bsCSlD744AOmTZuWbFlkZKSuT3NSnTt35r333gO0qvd///1X1w0qISGB6OhozM3NuXbtGnPmzGHfvn0AlCxZkhkzZhAdHc3NmzcZOnQokyZNomTJkrz++uvs3buXli1bcuPGDaKiopg7dy779u3DxsaGatWq8dZbb2Fubo6VlRVXr17lp59+Yu/evZibm+Pi4sJ3331HQEAAZcuWBbQW2U+ePOHWrVtER0cTExPDjRs3iI6OxtramgEDBmBubs6MGTMoVKgQ5ubmTJo0iffee4/79++zePFinj59mqpcRqW3fQ8PD/7++28mT57MwIED6d27t97tBwYG4uvrqyuTWdHR0QQFBbFq1Src3Nzo0aMHR48exd3dXQapEULkeSZfAwBw7do1XFxc+OGHH7h+/Tp9+/bFz8+PTz75hH///Zfz588zfPhwFi5cqHtNvnz52Lt3b6q/xJM/gLu7O/Xq1aNnz5588803mJubc+bMGapWrcr69et58803sba2xtramlKlSuHo6MiZM2do06YNLi4uxMbGUrJkSRo3bgzA06dPKV26NOvWrePtt9/G1tYWMzMzbGxsCAgIoGrVqgCsW7eOIUOGYGtri5WVFcWLF6dYsWLJ9jnxBPzjjz/ywQcfEBERwezZsxk9ejSLFi3i+++/Z/jw4axfv173mpo1a7Jjxw5Gjx6Nq6trmuXCwsJo1qwZzZo1Y9u2bfTu3ZtmzZoxffr0DG0foEGDBnz44YfJYk65fX1lMrp90Eama9++PW5ubgDY2dlhZ2eHUgozM7MMfHOEECL3MvkE4M6dO5QoUQLQBicJDAwkX7583LlzBxsbG06fPs2IESOYM2cOR44c0b0uMjJSd5JJ+vf999/rykRFRfG///2P1atXs2TJEh49esSJEyeoWbMmDx480J1kli5dqqt9OHHiBHXr1gW0blOJ96ejo6OxsLDAysqKmJgYYmJiANi6dStubm6cPn1ad5///v37xMXFATBv3jzKlCkDgKenp+5+eOHChbl16xZBQUHUqFGD8PBwTpw4gY+PD6GhoRQrVoxSpUpx+/ZtQOvj3adPH2bOnMn8+fMB9JZLfB8TE6K2bduyYsUK9u7dyzvvvKMrk9729dG3/bRkZPug3f+3tNQqwW7dusXt27epXr06ly9fpnLlyuluQwghcjuTvwVw/PhxatWqBWgnjl9++YWRI0cybdo0Jk+ezLp16+jfvz+3bt2ifPnyutcl1gCkZ+jQoQQFBREbG8vAgQMpXLgwJ0+eZNCgQTRv3pyePXuyfPly7O3tdSf9kydPMmDAAF1sderUAbSr1cQGim+++SZ9+/blp59+onjx4vzyyy98+umnDBo0CICBAwfSo0cPFi9eTEJCAg0aNACgQIECODo6cvfuXYoXL87FixeZPHkyAEFBQUyaNAnQGsKFhoZy69YtSpQoQXR0NL1792bixIk0adKEzZs389dff6Uqlxnm5uZpbj+ltLbfokWLTG0zpZMnT2JnZ0evXr2IiIjgp59+wszMjM2bNzNixIiXWrcQQuR4Sujs3LlT+fr6qhs3bqjGjRsrpZQaNWqUeu+999SwYcNUWFiYwbYVExOj+7ddu3bq7NmzBlt3eg4fPqw+++yzdMsEBgaqQYMGqUGDBqmHDx++dLkXceHCBdWpUyfVqlUrdfTo0Rcuk1mhoaGqd+/eBlmXEELkZDISoJF8+umnHDhwgLi4OIYOHYqvr6+xQxJCCGFCJAEQQgghTJDJNwIUQgghTJEkAEIIIYQJkgRACCGEMEGSAAghhBAmSBIAIYQQwgRJAiCEEEKYIEkAhBBCCBMkCYAQQghhgiQBEEIIIUyQJABCCCGECZIEQAghhDBBkgAIIYQQJkgSACGEEMIESQIghBBCmCBJAIQQQggTJAmAEEIIYYIkARBCCCFMkCQAQgghhAmSBEAIIYQwQZIACCGEECZIEgAhhBDCBEkCIIQQQpggSQCEEEIIEyQJgBBCCGGCJAEQQgghTJAkAEIIIYQJkgRACCGEMEGSAAghhBAmSBIAIYQQwgRJAiCEEEKYIEkAhBBCCBMkCYAQQghhgiQBEEIIIUyQJABCCCGECZIEQAghhDBBkgAIIYQQJkgSACGEEMIESQIghBBCmCBJAIQQQggTJAmAEEIIYYIkARBCCCFMkCQAQgghhAmSBEAIIYQwQbkyAXB1dcXCwgIrKyscHBxo06YNmzdvNnZYQggT4erqyq5du4y63mbNmvHrr79meN3+/v44Ojpy9uxZ3bJKlSpRunRpzp07p1uWkJBA165deeeddzIeuMiVcmUCALB9+3aio6O5cOECvXv3ZujQoXz//ffGDksIIXKchIQEBg8ezIwZM6hSpYpu+dmzZ3F3d2ft2rW6Zebm5ixZsoQ1a9Zw5MgRY4QrskmuTQBA+6IWL16c119/nV27dvG///2P27dvEx0dzfvvv4+LiwuOjo4MHz6cqKgo3et+/PFHypYtS8GCBalRowalS5fWPefq6spHH32Et7c3tra2HDx48LnrW7BgAZUqVaJgwYK0adOG69evZ+v7IIQwnqFDh1KsWDFsbW1xc3Nj0aJFuudcXV0ZO3YstWvXxs7Ojp49e3LgwAFatGhBwYIF8fb2TnW82LRpE7Vq1aJQoUL06dOHp0+fAnD16lVat26Nvb09lStX5uLFixmO448//sDKyoq+ffsme42FhQWNGzfm9OnTyZYXKFCAzz77jClTphjgHRI5Va5OAJKqVKkStWvXZuvWrXz00UcEBARw/Phxzp8/z9mzZ3W1A6tXr+bHH39k06ZNhISE8Prrr6da1+nTp1m8eDHh4eHUq1cv3fX98ccffPnll6xatYo7d+5QqVIlhgwZkq37LoQwnlGjRnHx4kUiIyP5448/GDVqFLdv39Y9f+XKFVasWEFQUBB+fn4MHz6cyZMnc+vWLYoXL853332XbH13795lzZo1XL16lX///ZfvvvtOVy3fqFEjQkJC2LlzJ46OjhmOY/Xq1QwaNChV7JGRkaxYsYJ//vkn1XO9e/dmz549hIeHG+JtEjmRyoXKli2rdu7cmWp5z5491ZQpU5SdnZ0KDAzULV+5cqVq1KiRUkqp9u3bqxkzZuie27NnjypVqlSa605ISEh3fW3btlXz58/XPXf37l1lbm6uoqOjDbCnQoicKOlx4sCBA6p3796qbNmyqmDBggpQe/bsSVVOKaW6d++uJk6cqHs8c+ZM1a5dO73rVUqppUuXqvr166ujR48qJycnFR8fr3vOx8dH/fLLL7rH6cXh6uqq/vnnn1T78e6776pmzZopMzMz9eTJk1TP16lTR+3bty+T747ILfJMDQDAnTt3sLCwICIiAi8vL2xtbbG1tWXAgAGEhIQAWjVahQoVMrzO0NDQdNf377//8tZbb+meK1OmDGZmZty9ezdL9lEIkXMEBwfzyiuvUL16dQ4dOsT9+/cpW7YscXFxesvb2tqilEr2OCYmJs31Ozk5cf/+fYKCgihbtizm5voP2c+L4+7du5QpUybZa/7++29WrVrF2rVrKVSoULLGgYlcXFzkWJaH5ZkE4OLFi5w8eRJfX19sbW25fPkyUVFRREVFER0dzaVLlwAoWrQo9+/fz/B6HR0d011fqVKlmD9/vu65qKgo4uLicHFxyZL9FELkHEePHsXDw4OPP/6YUqVKYWlpadD1BwcHU65cORwdHQkNDX2pOCwsLHT/j4qKYvDgwfz88884ODhQvXr1VO0AQGs8KPKuXJ0AKKW4d+8eixcvplWrVkyZMgUnJycGDhzI8OHDuXr1KtHR0Zw+fZpVq1YB0L59e+bNm8e9e/e4ceMGS5YsSXcb5ubm6a5v8ODBfP755xw9epTY2FiuXr3K3Llzs3zfhRDGV6FCBYKDg7ly5QoRERF8++233Lx586XWee7cOWJiYggICOC7775j8ODB1K9fnydPnrBs2TIiIiLYuHEj58+fz3AcxYsXT/Z4/PjxNGjQgI4dOwJQo0YNve0Arl+/jpOT00vtj8i5cm0C0KZNG6ysrHBzc2PZsmXMmzePUaNGAfDDDz/g5eVFy5YtKVKkCP369SMsLAyAd999l3LlylGuXDkaNWqEtbU11tbW6W4rvfX16dOHjz/+mCFDhlCoUCGaN2/OmTNnsnbnhRA5Qp06dRg9ejR16tShfPny3LlzJ1XjvMxasmQJxYoVo0WLFgwePJhevXqRP39+VqxYwcSJEyldujQrVqzA2dk5w3HUr1+fffv2AeDn58fq1av54YcfdM/XqFEjVQ3AvXv3CAgIoHbt2i+1PyLnMlNJb0iZoHXr1vHjjz+yf/9+Y4cihBBZYs2aNXz33Xf8/fffGX7N9OnT2blzJ5s2bcrCyIQxmVwCcPfuXc6ePYu3tzePHz+mX79+dOjQgffff9/YoeU4ibUcIm2FChUydghCPFdCQgLVqlVj0qRJdOvW7bnlHz16hLu7O3/++ScNGjTIhgiFMRi2xUoWM8QJ6fbt2wwZMoRbt25RrFgxunTpwsCBA+Vkl4Sc1AxHvldZL7u+r7n9s5wxYwb9+vWjXLlylC9fPs1ycXFxvPbaa3Tr1g0vL69s3285/mSfXFUDkNt/gLlF4g9Q3u/ne97BSt7DrCcJQN4iCUD2yVU1AMJ4Ci+TH2WiR33lRCCEyP1ybQIgWaJhydVN1pPvrOEY+/sqn6VhGfvzNFW5thugEEIIIV6cJABCCCGECZIEQAghhDBBkgAIIYQQJkgSACGEEMIESQIghBBCmKBcnwBs27ZNNzNfohs3bjB+/PgXmsryxIkTVKtWzVDhCSHyMEMef7Zs2UKVKlXw8PBgypQpL1xGiIzKteMAJAoICGDs2LGsWrWKKlWq8PjxYxYvXoy9vT2WlpaMHz8+w+saO3YsixYtokSJElkYsRAirzDU8efp06cMHz6co0eP4ujoSPPmzWnbti21atXKVBkhMiPX1wCMGTOGli1b8scff+Dk5MTGjRvx8vLi3Llzmc7Av/vuO44fP55FkQoh8hpDHX/8/PyoVasWzs7OWFpa0qNHD7Zs2ZLpMkJkRq6vAQBo2LAh3t7evP3223h4eLB161bs7e2NHZYQwgQY4vhz69YtnJycdI+LFSvG5cuXM11GiMzI9TUAAI8fP2bFihWUK1eOy5cvs3nzZmOHJIQwEYY6/pibJz8cx8TEvFAZITIq19cA3Lhxg8WLF+Pl5cXWrVuZO3cu/fr1M3ZYQggTYKjjj7OzM/fu3dM9Dg0NxdnZOdNlhMiMXF8DMGXKFOzt7XXVbmPHjuXrr79mzJgxkh0b0/ntMMENRtvAxs9gekvYMkl77u9F8F3jzK3v0l74pHTGyi7xhQ3/y9z6hXgBhjr+eHt74+/vT0hICHFxcaxZs4aWLVsSFhbGtWvX0i0jxIvK9TUA33//PcWLF+fbb79NtnzAgAEULFgwU+saP348f/zxB4GBgdSpU4fvvvsOHx8fQ4ZrOv74GJq/Az5vQ3wsWFobOyIhDM5Qxx97e3tmzpxJ8+bNiY2NpX///vj4+LBo0SIWLVrE3r170ywjxIsyU0opYweRUUmnjJTpOA1L33ubdFnhZZl4v5cOgb8XgJk5mJnBZ+dg2TCo7wvuzWF8eVAJYG6hlf/hKVjZpF7Pru/grx8h+gkU94RHN2DKDe2534fC6fUQ9QSKuEDbT6GBLxxZAkt9tTJm5lCuPow9mHb5F/Cob8a/h/re16+++oqoqKgX2ratrS0ff/zxC702LzHGsUCOP1lH3lvjyPU1ACIHGjAfLu6G/r+CZ6vkzxUtC/3nw+FftRNzWk6shn2zYeRWcHKHwwtg2xfPnm82Crp8BXZF4PZ5mFYfvNpA/YFw6S8oXBo6Z6B8oewf8yEqKoqJEye+0GtTvs7c3Bx3d3cAXF1dWbp0KcWKFXvJCA1j3Lhx1KtXj65duxo7FCGEHpIAiJzp74XQaiyUrKI9dvZM/nxkGGyfAlf/hoiHEB0Ody+mfULPbPlcws7OjoCAAAC+/vprxo8fz5w5c4wclWbq1KnGDkEIkY5c3whQ5FH3gqBoOf3P3Q+Gma9Aqerw/iGYdh8cykJCnGHK51L169fXNRgDmDRpEjVq1MDd3Z1vvvkGgPj4eEaNGoWbmxt169alUKFC3Lt3j+DgYMqWLUv//v2pXLky27dvJyAggJYtW1K5cmWaNm1KUFAQAB988AEVKlTA1dWVDz/8EIAffvgBNzc3ypUrR9++fQEYOXIkixYtAmD//v3UrFkTT09Phg0bRmxsLKDVWgwdOpQqVapQqVIlzp8/n11vlxAmTxKAJKKiomjVqhUVKlTA3d1dxtrOKuYW8LymJ/aOEB6q/7mgo1DcA9p8DIVLgUWKiiyzFOt/Xvk8IDY2lsWLF9OqlXbLZc2aNYSFhXHy5EnOnj3L6tWruXjxIvPnz+fRo0dcunSJQ4cOJRuw5unTp3zyySecO3eOV155hWHDhjF//nzOnTvHmDFj+PTTTzl9+jS7d+/mypUrXL16lc6dO/Po0SO++uorzp07R1BQEG+99Vay2KKjoxk0aBCrVq0iICCAuLg45s6dq3u+bdu2nD17luHDhzN9+vTsecNyoOeN8y/HJ2FoeeJImFajqhdpMPXRRx/RunVrIiMjqV+/Pu3bt6dGjRoGilQAWiO8Oxe0q3xLG60a3swseZkqHWD/HKjcHuKitTYAiYpV0K7qQ65A4ZKw7yd4dDP5+i/9BRGPICbi+eVzsYiICDw9Pbl16xYdO3bknXfeAbRJanbs2KEblCY8PJyrV6+yc+dO3nrrLczMzLC2tsbKykq3LmdnZ7y8vAC4efMm/v7+tG3bFoCEhARKlSpF6dKluXv3LqNGjaJp06Z07NgRKysrChcuzLBhw2jevDmdOnVKFmNAQABlypTBzc0NgP79+zNz5kxGjhwJQNOmTQGoXLkyO3fuzMJ3K2sY4viT0XH+5fgkDClPJABpNarKbEMrW1tbWrduDUC+fPmoWLEid+/eNUCEIhk3H+0E/0UVsC0AX1xL3U2wxRgtSZhQEYqU1noPJCpbB5qPhq/rgJUt1O2v1RgkavIWXNgBHxcHr3bw1h/pl8/FEtsAHD58mD59+nDnzh1KlixJQkIC06ZNo1evXsnK//TTT1hbP79LZkJCAmXLluXChQupnjtz5gybNm3ijz/+4Mcff+Tw4cOcOHGCzZs3s2fPHiZMmMClS5fSXLdSCrOUCR9gYWFBLuqUpGOI40/Scf4B3Tj/SRMAOT4JQ8sTCUBWuHv3LkeOHGH+/PnGDiV3+iI4+eN39z77v5kZ+C4Flqb9eitbGLQ4+bJes579v+Pn2l+i7kn6YRdyhg8OJ39teuXzgIYNG/L+++/TvXt39u3bR+vWrZk1axYdOnTA3t6eU6dOUbx4cWrWrMm2bdto0qQJQUFBPH78WO/6XFxcAFi/fj1du3YlMjISPz8/SpQoQUREBAMHDqRTp064u7sTEhLChQsXeO211+jSpQtly5YlIiJCty4PDw+uXbtGYGAgFSpUYPny5dJ/PYXMjvMvxydhCNIGQI/o6Gh69uzJl19+SeHChY0djhAZMmrUKCpUqMDIkSPp06cPrVq1ok6dOpQrV46RI0eilGLMmDH4+fnh4eHB6NGjMTc3J3/+/KnWZWZmxpo1a5g+fToeHh54eXlx4sQJEhISePPNN6lQoQI+Pj7Mnj0bMzMzxo8fT7ly5ahZsyaffPJJsr7ctra2LFy4kO7du+Ph4YFSKlU7AZHxcf7l+CQMRWoAUoiJiaF79+60a9cOX19fY4cjRLrCw8OTPf7tt990/58wYQITJkxI9nxsbCxbt27F0tKSa9eu8eqrr5IvXz5cXV05e/ZssrKVK1dm7969qbbp7++fatm+fftSLZs161mNTbNmzTh16lSqMsHBwcnKNGvWLFUZU5DRcf7l+CQMSRKAJCIiIujatSstWrTgo48+MnY4Io+ytbV94YGAbG1tX2rb169fp3Xr1lhbW1OwYEF++umnl1qfMAxvb2+GDBlCSEgIDg4OrFmzhi+++IKwsDDCwsIoU6aMHJ+EweWJBCCtA2pmD5Z+fn7s3buXf//9l4ULFwLQtWtXGdBEGJQxh/ItX748gYGBRtt+XmSI409G5gKQ45MwNJkLQAAGngsgj3vZuQDEy5O5APIWeW+NQxoBCiGEECZIEgAhhBDCBEkCIIQQQpggSQBS6N+/P25ubri5udG9e3eePn1q7JCEECbgeXMBJDVt2jSqVKmSTZGJvCpP9AIw5FwAvr6+LF26FDMzM/r27cvq1aulv60QIk3ZORcAwKFDh1i2bNlLxy1EnkgADDUXAKCbTe3p06eEhoZSqVKll4xOCJGXZddcAAD37t3j3Xff5eeff2bIkCEvE7YQcgtAnwULFuDs7Ez16tWpV6+escMRQuRx+uYCuHPnTrIySikGDRrEN998Q/HixbM7RJEHSQKgx+DBg3n48CEhISEsXrz4+S8QIhNCQkKIj4/P9Otu376dBdGInOJ5cwH88MMPNGzY0GSHSxaGlyduAWQFS0tLWrVqhZ+fn7QBEAbVsGFD4uPj6datGwUKFHhu+djYWLZs2cKVK1f48MMP+eyzz7IhSpGdMjIXQFBQEDt27GDp0qXExsZy48YNmjRpwoEDB7I7XJFHSAKQxMOHDzl27BitW7cmNjaWP/74g86dOxs7LJHHdOjQgb///pvffvuN33//XdfuRJ8LFy7Qs2dPHj9+zI4dO9i+fXs2RiqyS0bmApg5c6aufHBwMB07dpSTv3gpcgsgCaUUU6ZMwdXVFS8vL1xdXRk4cKCxwxJ5TJEiRTh48CB9+/alTZs2TJgwQe8tgaVLl+qm8z116hQNGjRIVeb+/fsMHDiQ8uXLU6ZMGdq0acORI0f0bjc4ODjbWo+3bNmSmzdvZvp1rq6uya6ETUXSuQC8vLxo3bo1Pj4+rF+/Xo5BIsvkiRoAQ00G5ODgwJ49ewwUVd6SdPx78fKsra354Ycf8PHxwdfXlwMHDvD7779TokQJIiIiGDVqFEuWLOGrr77ivffew8zMLNU6lFJ07NiRIUOGsGTJEpRS7N+/n0uXLlG/fv1U5RMTgL59+2b5/u3evTvLt5FTGOr406lTJzp16pRsma+vr95bkPqmbxYis/JEAmDM2dWEeBldunTh5MmT9OrVixo1ajBlyhR++OEHHj9+zP79+/Ve9Sfas2cP9vb2vPHGGwCYmZnh4+PDvXv38PT01JX77LPP6NevH+PGjePMmTN4enrSu3dvJk6cyKRJk1i3bh0RERG88cYbfPjhh8THxzNmzBi2bdtG4cKFuXTpEoGBgTg6OjJr1ixmz54NwKhRoxgxYgTBwcH4+PjQpEkTTp48yffff8/YsWPZtGkTrq6uLF68mGnTphEREUGdOnVYtWoVw4YNY/fu3ZiZmVGvXj0WL16MpWXuPBzJ8UfkVrnzFydEHlKuXDkOHjxI586deeONN3Bzc+P48eMUK1Ys3dedO3dObzfVggULcuTIEQoXLszNmzepW7cu/fr1Y+rUqXz77bds2rQJgDVr1hAWFsbJkyeJjY2lUaNGvPrqq+zbt49Hjx5x6dIlYmNjKVeuHACnTp3i119/5dixYwA0atSIxo0bU7BgQZ4+fconn3yCl5cXAGPHjtXF+M0333Do0CEKFy7M1q1bAZg8eTJz584FoG3btuzatYu2bdsa4N0UQmSUJAAiTTItZ/ZIrPLfvXs3vr6+rFu3jl69euluCaT3On3Mzc2ZPn06mzdvJjw8PFV/8kTbtm1jx44dbN68GYDw8HCuXr3Kzp07eeuttzAzM8Pa2horKysA9u3bx6uvvkr+/PkBePXVV9m7dy+dO3fG2dlZd/JP6q+//qJbt24ULlwYgHbt2gFw4sQJZs6cSVBQEHfu3OHu3bsZe7OEEAYjjQCFMKILFy5Qr149du7cyf79+1m4cCGnTp0iPDycGjVqsGvXrjRfW6VKFfz8/FItnzp1Kv/88w9btmzh/Pnz2NnZ6X19QkIC06ZNIyAggICAAG7cuEG7du2IiorC2tr6ubErpfS2TUhZJqXg4GAGDRrE559/zrlz5+jbt6/eckKIrCUJgB4y0YbIDmm18k+8JfC8XgJt2rTh5s2b/PTTT7plhw4dYunSpfj4+ODo6MiZM2d0A8oUKVKEGzduoJRCKUXr1q2ZNWsW4eHhgFbFf/v2bWrWrMm2bdsAre/548ePAWjatCkbNmwgIiKCiIgINm7cSNOmTdPdxyZNmrBu3TrCwrRGpAcOHODRo0eULl2a2rVrExERQUBAwEu+k3lDRiYDio2NZezYsVSsWBEXFxcePnyYzVGKvCRP3AIw5GRAMtGGyGqxsbG6lvtptfJPq5dAUpaWlmzbto133nmH6dOn8/TpU9zc3Jg/fz4jRozgp59+wtvbGwsLCwCqVatG2bJlKVu2LK+//jqff/45ly5dok6dOkRHR1OqVClWrVrFmDFj6NWrFx4eHri7u2Nubk7+/PmpWbMmvr6+uvHp3377bapXr05wcHCa+1qzZk3efvtt6tWrh1KKmjVrsnLlSqpWrUr58uUpUaKELr7cKjsnAxoxYgQlSpTg8uXLBoldmDiVizx69Ej3l9SECRP0lk9reVpCQ0NV3bp11ZEjR1TlypVfMMrcKa33Vrwcfe9rhQoVlIuLizp8+HCG1nH16lVVt25d5eLioj799NOsClUnJiZGxcbGKqWU+vfff1WNGjWyfJuZZYzva1Yef/766y/VpUsX3eMff/xRTZ48OVmZ27dvKy8vLxUfH5/h9eYWcvwxjjxRA2AISibaENmkffv25MuXj+3bt2d4ZL82bdpw48YN7O3tszg6uH79Oq1bt8ba2pqCBQsmu8Ugsoa+yYBSXuWfPXsWMzMzWrRowe3bt6lTpw7z5s3TNcoUIrMkAfhP0ok20qvSFOJlzZgxw9ghpKt8+fIEBgYaOwyT87zJgEJCQnB3d2f58uVYWlrywQcf8Pnnn/PNN99kZ5giD5FGgP8JCgpiyZIleHp60rJlSy5fvkyTJk2MHZYQwgRkZDKgIkWKkD9/fmxsbLCwsKBLly5cuHAhu0MVeYgkAP+ZOXMmFy9eJCAggN27d+Pm5iYTbQghsoW3tzf+/v6EhIQQFxfHmjVraNmyJWFhYVy7dg3QBl7av3+/roZy69ateHt7GzFqkdvliVsAhhqLWwghMssQx5+kkwHFxsbSv39/fHx8WLRoEYsWLWLv3r0ULFiQ+fPn8+qrrxIbG0uDBg2kfYZ4KWZK5Z4ROBL7EoOMUmdo8t5mDXlfs4Yx3lf5LLOOvLfGIbcAhBBCCBMkCYAQQghhgiQBEEIIIUyQJAApNGvWDFdXVzw9PfH09OSLL74wdkhCCBOQkbkAFi9eTJUqVXB3d6dHjx66eRyEeBF5oheAIecCAG2e9Dp16hgiNCFEHpddcwHcvXuXzz//nNOnT1OgQAFGjBjBzJkzGTdunMH2RZiWPJEAREVF6e2Go2+ZEEIYkiGOP35+ftSqVUs3+E+PHj3YsmVLsgQgJiaGp0+fEh4eToECBXB2ds7QtM1CpCVPJACGZGZmRo8ePbCxsaFNmzZ8//33WFrK2ySEyDoZmQvAxcWFd999l0qVKtGjRw/u3r3LqlWrsjtUkYdIG4AUtm7dSnBwMCdPnuT27dvMmzfP2CEJIUzA8+YCCAsLY8OGDfz999+0adOGq1ev8tdff2VniCKPkUvbFBJH77Kzs6NTp074+/sbOSIhRF6XkbkAdu7cSaVKlXR/9vb2zJ49mw4dOmR3uCKPkAQgiaioKI4cOUKzZs2IjY1l/fr19OjRw9hhiTwi6WhnQiTl7e3NkCFDCAkJwcHBgTVr1vDFF18QFhZGWFgYZcqUoXz58hw4cIAHDx7g4ODAsWPH8PT0NHboIhfLEwmAoeYCUEoxfvx4rl+/jrW1NZ06daJPnz4GilIIkRdl11wAtWrVYuTIkdSvXx8LCwtq1KghtyjFS5G5AAQg721Wkav+rCdzAeR+8t4aR56oARAip5KDmRAip5JeAEIIIYQJkgRACCGEMEGSACQRGxvL2LFjqVixIi4uLjx8+NDYIQkhTMiJEyeoVq2ascMQJiJPtAEw1FwAI0aMoESJEqlG4BJCiLQY6vgzduxYFi1aRIkSJQwZnhBpyhMJgCHG4r5z5w6HDx/mzJkzmJmZGS44IUSeZqi5SL777jtGjRpFx44dDROYEM8htwD+c/bsWczMzGjRogUeHh7069ePp0+fGjssIYQQIktIAvCfkJAQ3N3d2b59O+fPn6d48eJ8/vnnxg5LCCGEyBKSAPynSJEi5M+fHxsbGywsLOjSpQsXLlwwdlhCCCFElpAE4D+NGjVi//79BAcHA9qsgN7e3sYNSgghhMgikgD8p2DBgsyfP59XX30VLy8vQkJC+OCDD4wdlhDCRIwfP57OnTsTGBhInTp12Ldvn7FDEnlcnpgLwFDdcEyZjMUtRPrk+JN15PhjHHkiARAvT95bIdInv5GsI++tccgtACGEEMIESQIghBBCmCBJAIQQQggTJAmAEEIIYYIkARBCCCFMkCQAQgghhAnKtbMBJu02IoQQ2UmOPyIvkBoAIYQQwgRJAiCEEEKYoFw1EqAQQgghDENqAIQQQggTJAmAEEIIYYIkARBCCCFMkCQAQgghhAn6P1Ihy8LeoF9+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close(\"all\")\n",
    "display(output)\n",
    "ofit = overfit_example(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cb452d9",
   "metadata": {},
   "source": [
    "In the plot above, try out regularization on the previous example. In particular:\n",
    "- Categorical (logistic regression)\n",
    "    - set degree to 6, lambda to 0 (no regularization), fit the data\n",
    "    - now set lambda to 1 (increase regularization), fit the data, notice the difference.\n",
    "- Regression (linear regression)\n",
    "    - try the same procedure."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d23e3d5b",
   "metadata": {},
   "source": [
    "## Congratulations!\n",
    "You have:\n",
    "- examples of cost and gradient routines with regression added for both linear and logistic regression\n",
    "- developed some intuition on how regularization can reduce over-fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea612d4b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
